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Drafts by Loc Tran

Research paper thumbnail of To Detect Irregular Trade Behaviors In Stock Market By Using Graph Based Ranking Methods

To detect the irregular trade behaviors in the stock market is the important problem in machine l... more To detect the irregular trade behaviors in the stock market is the important problem in machine learning field. These irregular trade behaviors are obviously illegal. To detect these irregular trade behaviors in the stock market, data scientists normally employ the supervised learning techniques. In this paper, we employ the three graph Laplacian based semi-supervised ranking methods to solve the irregular trade behavior detection problem. Experimental results show that that the un-normalized and symmetric normalized graph Laplacian based semi-supervised ranking methods outperform the random walk Laplacian based semi-supervised ranking method.

Papers by Loc Tran

Research paper thumbnail of Hypergraph convolutional neural network-based clustering technique

IAES International Journal of Artificial Intelligence (IJ-AI)

This paper constitutes the novel hypergraph convolutional neural networkbased clustering techniqu... more This paper constitutes the novel hypergraph convolutional neural networkbased clustering technique. This technique is employed to solve the clustering problem for the Citeseer dataset and the Cora dataset. Each dataset contains the feature matrix and the incidence matrix of the hypergraph (i.e., constructed from the feature matrix). This novel clustering method utilizes both matrices. Initially, the hypergraph auto-encoders are employed to transform both the incidence matrix and the feature matrix from high dimensional space to low dimensional space. In the end, we apply the k-means clustering technique to the transformed matrix. The hypergraph convolutional neural network (CNN)-based clustering technique presented a better result on performance during experiments than those of the other classical clustering techniques.

Research paper thumbnail of Solve fraud detection problem by using graph based learning methods

The credit cards' fraud transactions detection is the important problem in machine learning f... more The credit cards' fraud transactions detection is the important problem in machine learning field. To detect the credit cards's fraud transactions help reduce the significant loss of the credit cards' holders and the banks. To detect the credit cards' fraud transactions, data scientists normally employ the unsupervised learning techniques and supervised learning techniques. In this paper, we employ the graph p-Laplacian based semi-supervised learning methods combined with the undersampling techniques such as Cluster Centroids to solve the credit cards' fraud transactions detection problem. Experimental results show that the graph p-Laplacian semi-supervised learning methods outperform the current state of the art graph Laplacian based semi-supervised learning method (p=2).

Research paper thumbnail of Un-normalized hypergraph p-Laplacian based semi-supervised learning methods

Most network-based machine learning methods assume that the labels of two adjacent samples in the... more Most network-based machine learning methods assume that the labels of two adjacent samples in the network are likely to be the same. However, assuming the pairwise relationship between samples is not complete. The information a group of samples that shows very similar pattern and tends to have similar labels is missed. The natural way overcoming the information loss of the above assumption is to represent the feature dataset of samples as the hypergraph. Thus, in this paper, we will present the un-normalized hypergraph p-Laplacian semi-supervised learning methods. These methods will be applied to the zoo dataset and the tiny version of 20 newsgroups dataset. Experiment results show that the accuracy performance measures of these un-normalized hypergraph p-Laplacian based semi-supervised learning methods are significantly greater than the accuracy performance measure of the un-normalized hypergraph Laplacian based semi-supervised learning method (the current state of the art method h...

Research paper thumbnail of Tensor Sparse PCA and Face Recognition: A Novel Approach

Face recognition is the important field in machine learning and pattern recognition research area... more Face recognition is the important field in machine learning and pattern recognition research area. It has a lot of applications in military, finance, public security, to name a few. In this paper, the combination of the tensor sparse PCA with the nearest-neighbor method (and with the kernel ridge regression method) will be proposed and applied to the face dataset. Experimental results show that the combination of the tensor sparse PCA with any classification system does not always reach the best accuracy performance measures. However, the accuracy of the combination of the sparse PCA method and one specific classification system is always better than the accuracy of the combination of the PCA method and one specific classification system and is always better than the accuracy of the classification system itself.

Research paper thumbnail of Directed hypergraph neural network

To deal with irregular data structure, graph convolution neural networks have been developed by a... more To deal with irregular data structure, graph convolution neural networks have been developed by a lot of data scientists. However, data scientists just have concentrated primarily on developing deep neural network method for un-directed graph. In this paper, we will present the novel neural network method for directed hypergraph. In the other words, we will develop not only the novel directed hypergraph neural network method but also the novel directed hypergraph based semi-supervised learning method. These methods are employed to solve the node classification task. The two datasets that are used in the experiments are the cora and the citeseer datasets. Among the classic directed graph based semi-supervised learning method, the novel directed hypergraph based semi-supervised learning method, the novel directed hypergraph neural network method that are utilized to solve this node classification task, we recognize that the novel directed hypergraph neural network achieves the highest...

Research paper thumbnail of To Detect Irregular Trade Behaviors In Stock Market By Using Graph Based Ranking Methods

To detect the irregular trade behaviors in the stock market is the important problem in machine l... more To detect the irregular trade behaviors in the stock market is the important problem in machine learning field. These irregular trade behaviors are obviously illegal. To detect these irregular trade behaviors in the stock market, data scientists normally employ the supervised learning techniques. In this paper, we employ the three graph Laplacian based semi-supervised ranking methods to solve the irregular trade behavior detection problem. Experimental results show that that the un-normalized and symmetric normalized graph Laplacian based semi-supervised ranking methods outperform the random walk Laplacian based semi-supervised ranking method.

Research paper thumbnail of Hypergraph based semi-supervised learning algorithms applied to speech recognition problem: a novel approach

ArXiv, 2018

Most network-based speech recognition methods are based on the assumption that the labels of two ... more Most network-based speech recognition methods are based on the assumption that the labels of two adjacent speech samples in the network are likely to be the same. However, assuming the pairwise relationship between speech samples is not complete. The information a group of speech samples that show very similar patterns and tend to have similar labels is missed. The natural way overcoming the information loss of the above assumption is to represent the feature data of speech samples as the hypergraph. Thus, in this paper, the three un-normalized, random walk, and symmetric normalized hypergraph Laplacian based semi-supervised learning methods applied to hypergraph constructed from the feature data of speech samples in order to predict the labels of speech samples are introduced. Experiment results show that the sensitivity performance measures of these three hypergraph Laplacian based semi-supervised learning methods are greater than the sensitivity performance measures of the Hidden...

Research paper thumbnail of The combination of Sparse Principle Component Analysis and Kernel Ridge Regression methods applied to speech recognition problem

Speech recognition is the important problem in pattern recognition research field. In this paper,... more Speech recognition is the important problem in pattern recognition research field. In this paper, the combination of the Sparse Principle Component Analysis method and the kernel ridge regression method will be applied to the MFCC feature vectors of the speech dataset available from IC Design lab at Faculty of Electricals-Electronics Engineering, University of Technology, Ho Chi Minh City. Experiment results show that the combination of the Sparse Principle Component Analysis method and the kernel ridge regression method outperforms the current state of the art Hidden Markov Model method and the kernel ridge regression method alone in speech recognition problem in terms of sensitivity performance measure.

Research paper thumbnail of Disease Gene Prioritization and the Novel Un-normalized Graph ( p-) Laplacian Ranking Methods

Abstract—The biological motivated problem that we want to solve in this paper is to predict the n... more Abstract—The biological motivated problem that we want to solve in this paper is to predict the new members of a partially known set of genes involved in specific disease (i.e. disease gene prioritization). In this problem, we are given a core set of genes (i.e. the queries) involved in the specific disease. However, the biologist experts do not know whether this core set is complete or not. Our objective is to find more potential members of this core set by ranking genes in gene-gene interaction network. One of the solutions to this problem is the random walk on graphs method. However, the random walk on graphs method is not the current state of the art network-based method solving bioinformatics problem. In this paper, the novel un-normalized graph (p-) Laplacian based ranking method will be developed based on the un-normalized graph p-Laplacian operator definitions such as the curvature operator of graph (i.e. the un-normalized graph 1-Laplacian operator) and will be used to solv...

Research paper thumbnail of The Un-normalized Graph p-Laplacian based Semi-supervised Learning Method and Speech Recognition Problem

Speech recognition is the classical problem in pattern recognition research field. However, just ... more Speech recognition is the classical problem in pattern recognition research field. However, just a few graph based machine learning methods have been applied to this classical problem. In this paper, we propose the un-normalized graph p-Laplacian semi-supervised learning methods and these methods will be applied to the speech network constructed from the MFCC speech dataset to predict the labels of all speech samples in the speech network. These methods are based on the assumption that the labels of two adjacent speech samples in the network are likely to be the same. The experiments show that that the un-normalized graph p-Laplacian semi-supervised learning methods are at least as good as the current state of the art method (the un-normalized graph Laplacian based semi-supervised learning method) but often lead to better classification sensitivity performance measures.

Research paper thumbnail of Un-normalized hypergraph p-Laplacian based semi-supervised learning methods

Most network-based machine learning methods assume that the labels of two adjacent samples in the... more Most network-based machine learning methods assume that the labels of two adjacent samples in the network are likely to be the same. However, assuming the pairwise relationship between samples is not complete. The information a group of samples that shows very similar pattern and tends to have similar labels is missed. The natural way overcoming the information loss of the above assumption is to represent the feature dataset of samples as the hypergraph. Thus, in this paper, we will present the un-normalized hypergraph p-Laplacian semi-supervised learning methods. These methods will be applied to the zoo dataset and the tiny version of 20 newsgroups dataset. Experiment results show that the accuracy performance measures of these un-normalized hypergraph p-Laplacian based semi-supervised learning methods are significantly greater than the accuracy performance measure of the un-normalized hypergraph Laplacian based semi-supervised learning method (the current state of the art method h...

Research paper thumbnail of Solve fraud detection problem by using graph based learning methods

Journal of Engineering and Science Research

The credit cards' fraud transactions detection is the important problem in machine learning field... more The credit cards' fraud transactions detection is the important problem in machine learning field. To detect the credit cards' fraud transactions help reduce the significant loss of the credit cards' holders and the banks. To detect the credit cards' fraud transactions, data scientists normally employ the un-supervised learning techniques and supervised learning technique. In this paper, we employ the graph p-Laplacian based semi-supervised learning methods combined with the under-sampling technique such as Cluster Centroids to solve the credit cards' fraud transactions detection problem. Experimental results show that that the graph p-Laplacian semi-supervised learning methods outperform the current state of art graph Laplacian based semi-supervised learning method (p=2).

Research paper thumbnail of Tensor sparse PCA and face recognition: a novel approach

SN Applied Sciences

Face recognition is the important field in machine learning and pattern recognition research area... more Face recognition is the important field in machine learning and pattern recognition research area. It has a lot of applications in military, finance, public security, to name a few. In this paper, the combination of the tensor sparse PCA with the nearest-neighbor method (and with the kernel ridge regression method) will be proposed and applied to the face dataset. Experimental results show that the combination of the tensor sparse PCA with any classification system does not always reach the best accuracy performance measures. However, the accuracy of the combination of the sparse PCA method and one specific classification system is always better than the accuracy of the combination of the PCA method and one specific classification system and is always better than the accuracy of the classification system itself.

Research paper thumbnail of Directed Hypergraph Neural Network

Journal of Advanced Research in Dynamical and Control Systems

To deal with irregular data structure, graph convolution neural networks have been developed by a... more To deal with irregular data structure, graph convolution neural networks have been developed by a lot of data scientists. However, data scientists just have concentrated primarily on developing deep neural network method for un-directed graph. In this paper, we will present the novel neural network method for directed hypergraph. In the other words, we will develop not only the novel directed hypergraph neural network method but also the novel directed hypergraph based semi-supervised learning method. These methods are employed to solve the node classification task. The two datasets that are used in the experiments are the cora and the citeseer datasets. Among the classic directed graph based semi-supervised learning method, the novel directed hypergraph based semisupervised learning method, the novel directed hypergraph neural network method that are utilized to solve this node classification task, we recognize that the novel directed hypergraph neural network achieves the highest accuracies.

Research paper thumbnail of Combinatorial and Random Walk Hypergraph Laplacian Eigenmaps

International Journal of Machine Learning and Computing, 2015

Most network-based machine learning methods are based on the assumption that the labels of two ad... more Most network-based machine learning methods are based on the assumption that the labels of two adjacent vertices in the network are likely to be the same. However, assuming the pairwise relationship between vertices is not complete. The information a group of vertices that show very similar patterns and tend to have similar labels is missed. The natural way overcoming the information loss of the above assumption is to represent the given data as the hypergraph. However, representing the dataset as the hypergraph will not lead to the perfection. The number of hyper-edges may be large; hence this will lead to high time complexity of the clustering methods or the classification methods when we try to apply the clustering/classification methods to this hypergraph dataset. Thus, there exists a need to develop the dimensional reduction methods for the hypergraph datasets. In this paper, the two un-normalized and random walk hypergraph Laplacian Eigenmaps are introduced. Experiment results show that the accuracy performance measures of these two hypergraph Laplacian Eigenmaps combined with graph based semi-supervised learning method are greater than the accuracy performance measure of graph based semi-supervised learning method alone (i.e. the baseline method of this paper) applied to the original hypergraph datasets.

Research paper thumbnail of Un-Normalized Graph P-Laplacian Semi-Supervised Learning Method Applied to Cancer Classification Problem

Journal of Automation and Control Engineering, 2015

Research paper thumbnail of Hypergraph and Protein Function Prediction with Gene Expression Data

Journal of Automation and Control Engineering, 2015

Research paper thumbnail of Kernel ridge regression method applied to speech recognition problem: A novel approach

2014 International Conference on Advanced Technologies for Communications (ATC 2014), 2014

Speech recognition is the important problem in pattern recognition research field. In this paper,... more Speech recognition is the important problem in pattern recognition research field. In this paper, the kernel ridge regression method is proposed to be applied to the MFCC feature vectors of the speech dataset available from IC Design lab at Faculty of Electricals-Electronics Engineering, University of Technology, Ho Chi Minh City. Experiment results show that the kernel ridge regression method outperforms the current state of the art Hidden Markov Model method in speech recognition problem in terms of sensitivity performance measure and calculation speed of training process.

Research paper thumbnail of The Un-normalized Graph p-Laplacian Based Semi-supervised Learning Method and Protein Function Prediction Problem

Advances in Intelligent Systems and Computing, 2014

ABSTRACT

Research paper thumbnail of To Detect Irregular Trade Behaviors In Stock Market By Using Graph Based Ranking Methods

To detect the irregular trade behaviors in the stock market is the important problem in machine l... more To detect the irregular trade behaviors in the stock market is the important problem in machine learning field. These irregular trade behaviors are obviously illegal. To detect these irregular trade behaviors in the stock market, data scientists normally employ the supervised learning techniques. In this paper, we employ the three graph Laplacian based semi-supervised ranking methods to solve the irregular trade behavior detection problem. Experimental results show that that the un-normalized and symmetric normalized graph Laplacian based semi-supervised ranking methods outperform the random walk Laplacian based semi-supervised ranking method.

Research paper thumbnail of Hypergraph convolutional neural network-based clustering technique

IAES International Journal of Artificial Intelligence (IJ-AI)

This paper constitutes the novel hypergraph convolutional neural networkbased clustering techniqu... more This paper constitutes the novel hypergraph convolutional neural networkbased clustering technique. This technique is employed to solve the clustering problem for the Citeseer dataset and the Cora dataset. Each dataset contains the feature matrix and the incidence matrix of the hypergraph (i.e., constructed from the feature matrix). This novel clustering method utilizes both matrices. Initially, the hypergraph auto-encoders are employed to transform both the incidence matrix and the feature matrix from high dimensional space to low dimensional space. In the end, we apply the k-means clustering technique to the transformed matrix. The hypergraph convolutional neural network (CNN)-based clustering technique presented a better result on performance during experiments than those of the other classical clustering techniques.

Research paper thumbnail of Solve fraud detection problem by using graph based learning methods

The credit cards' fraud transactions detection is the important problem in machine learning f... more The credit cards' fraud transactions detection is the important problem in machine learning field. To detect the credit cards's fraud transactions help reduce the significant loss of the credit cards' holders and the banks. To detect the credit cards' fraud transactions, data scientists normally employ the unsupervised learning techniques and supervised learning techniques. In this paper, we employ the graph p-Laplacian based semi-supervised learning methods combined with the undersampling techniques such as Cluster Centroids to solve the credit cards' fraud transactions detection problem. Experimental results show that the graph p-Laplacian semi-supervised learning methods outperform the current state of the art graph Laplacian based semi-supervised learning method (p=2).

Research paper thumbnail of Un-normalized hypergraph p-Laplacian based semi-supervised learning methods

Most network-based machine learning methods assume that the labels of two adjacent samples in the... more Most network-based machine learning methods assume that the labels of two adjacent samples in the network are likely to be the same. However, assuming the pairwise relationship between samples is not complete. The information a group of samples that shows very similar pattern and tends to have similar labels is missed. The natural way overcoming the information loss of the above assumption is to represent the feature dataset of samples as the hypergraph. Thus, in this paper, we will present the un-normalized hypergraph p-Laplacian semi-supervised learning methods. These methods will be applied to the zoo dataset and the tiny version of 20 newsgroups dataset. Experiment results show that the accuracy performance measures of these un-normalized hypergraph p-Laplacian based semi-supervised learning methods are significantly greater than the accuracy performance measure of the un-normalized hypergraph Laplacian based semi-supervised learning method (the current state of the art method h...

Research paper thumbnail of Tensor Sparse PCA and Face Recognition: A Novel Approach

Face recognition is the important field in machine learning and pattern recognition research area... more Face recognition is the important field in machine learning and pattern recognition research area. It has a lot of applications in military, finance, public security, to name a few. In this paper, the combination of the tensor sparse PCA with the nearest-neighbor method (and with the kernel ridge regression method) will be proposed and applied to the face dataset. Experimental results show that the combination of the tensor sparse PCA with any classification system does not always reach the best accuracy performance measures. However, the accuracy of the combination of the sparse PCA method and one specific classification system is always better than the accuracy of the combination of the PCA method and one specific classification system and is always better than the accuracy of the classification system itself.

Research paper thumbnail of Directed hypergraph neural network

To deal with irregular data structure, graph convolution neural networks have been developed by a... more To deal with irregular data structure, graph convolution neural networks have been developed by a lot of data scientists. However, data scientists just have concentrated primarily on developing deep neural network method for un-directed graph. In this paper, we will present the novel neural network method for directed hypergraph. In the other words, we will develop not only the novel directed hypergraph neural network method but also the novel directed hypergraph based semi-supervised learning method. These methods are employed to solve the node classification task. The two datasets that are used in the experiments are the cora and the citeseer datasets. Among the classic directed graph based semi-supervised learning method, the novel directed hypergraph based semi-supervised learning method, the novel directed hypergraph neural network method that are utilized to solve this node classification task, we recognize that the novel directed hypergraph neural network achieves the highest...

Research paper thumbnail of To Detect Irregular Trade Behaviors In Stock Market By Using Graph Based Ranking Methods

To detect the irregular trade behaviors in the stock market is the important problem in machine l... more To detect the irregular trade behaviors in the stock market is the important problem in machine learning field. These irregular trade behaviors are obviously illegal. To detect these irregular trade behaviors in the stock market, data scientists normally employ the supervised learning techniques. In this paper, we employ the three graph Laplacian based semi-supervised ranking methods to solve the irregular trade behavior detection problem. Experimental results show that that the un-normalized and symmetric normalized graph Laplacian based semi-supervised ranking methods outperform the random walk Laplacian based semi-supervised ranking method.

Research paper thumbnail of Hypergraph based semi-supervised learning algorithms applied to speech recognition problem: a novel approach

ArXiv, 2018

Most network-based speech recognition methods are based on the assumption that the labels of two ... more Most network-based speech recognition methods are based on the assumption that the labels of two adjacent speech samples in the network are likely to be the same. However, assuming the pairwise relationship between speech samples is not complete. The information a group of speech samples that show very similar patterns and tend to have similar labels is missed. The natural way overcoming the information loss of the above assumption is to represent the feature data of speech samples as the hypergraph. Thus, in this paper, the three un-normalized, random walk, and symmetric normalized hypergraph Laplacian based semi-supervised learning methods applied to hypergraph constructed from the feature data of speech samples in order to predict the labels of speech samples are introduced. Experiment results show that the sensitivity performance measures of these three hypergraph Laplacian based semi-supervised learning methods are greater than the sensitivity performance measures of the Hidden...

Research paper thumbnail of The combination of Sparse Principle Component Analysis and Kernel Ridge Regression methods applied to speech recognition problem

Speech recognition is the important problem in pattern recognition research field. In this paper,... more Speech recognition is the important problem in pattern recognition research field. In this paper, the combination of the Sparse Principle Component Analysis method and the kernel ridge regression method will be applied to the MFCC feature vectors of the speech dataset available from IC Design lab at Faculty of Electricals-Electronics Engineering, University of Technology, Ho Chi Minh City. Experiment results show that the combination of the Sparse Principle Component Analysis method and the kernel ridge regression method outperforms the current state of the art Hidden Markov Model method and the kernel ridge regression method alone in speech recognition problem in terms of sensitivity performance measure.

Research paper thumbnail of Disease Gene Prioritization and the Novel Un-normalized Graph ( p-) Laplacian Ranking Methods

Abstract—The biological motivated problem that we want to solve in this paper is to predict the n... more Abstract—The biological motivated problem that we want to solve in this paper is to predict the new members of a partially known set of genes involved in specific disease (i.e. disease gene prioritization). In this problem, we are given a core set of genes (i.e. the queries) involved in the specific disease. However, the biologist experts do not know whether this core set is complete or not. Our objective is to find more potential members of this core set by ranking genes in gene-gene interaction network. One of the solutions to this problem is the random walk on graphs method. However, the random walk on graphs method is not the current state of the art network-based method solving bioinformatics problem. In this paper, the novel un-normalized graph (p-) Laplacian based ranking method will be developed based on the un-normalized graph p-Laplacian operator definitions such as the curvature operator of graph (i.e. the un-normalized graph 1-Laplacian operator) and will be used to solv...

Research paper thumbnail of The Un-normalized Graph p-Laplacian based Semi-supervised Learning Method and Speech Recognition Problem

Speech recognition is the classical problem in pattern recognition research field. However, just ... more Speech recognition is the classical problem in pattern recognition research field. However, just a few graph based machine learning methods have been applied to this classical problem. In this paper, we propose the un-normalized graph p-Laplacian semi-supervised learning methods and these methods will be applied to the speech network constructed from the MFCC speech dataset to predict the labels of all speech samples in the speech network. These methods are based on the assumption that the labels of two adjacent speech samples in the network are likely to be the same. The experiments show that that the un-normalized graph p-Laplacian semi-supervised learning methods are at least as good as the current state of the art method (the un-normalized graph Laplacian based semi-supervised learning method) but often lead to better classification sensitivity performance measures.

Research paper thumbnail of Un-normalized hypergraph p-Laplacian based semi-supervised learning methods

Most network-based machine learning methods assume that the labels of two adjacent samples in the... more Most network-based machine learning methods assume that the labels of two adjacent samples in the network are likely to be the same. However, assuming the pairwise relationship between samples is not complete. The information a group of samples that shows very similar pattern and tends to have similar labels is missed. The natural way overcoming the information loss of the above assumption is to represent the feature dataset of samples as the hypergraph. Thus, in this paper, we will present the un-normalized hypergraph p-Laplacian semi-supervised learning methods. These methods will be applied to the zoo dataset and the tiny version of 20 newsgroups dataset. Experiment results show that the accuracy performance measures of these un-normalized hypergraph p-Laplacian based semi-supervised learning methods are significantly greater than the accuracy performance measure of the un-normalized hypergraph Laplacian based semi-supervised learning method (the current state of the art method h...

Research paper thumbnail of Solve fraud detection problem by using graph based learning methods

Journal of Engineering and Science Research

The credit cards' fraud transactions detection is the important problem in machine learning field... more The credit cards' fraud transactions detection is the important problem in machine learning field. To detect the credit cards' fraud transactions help reduce the significant loss of the credit cards' holders and the banks. To detect the credit cards' fraud transactions, data scientists normally employ the un-supervised learning techniques and supervised learning technique. In this paper, we employ the graph p-Laplacian based semi-supervised learning methods combined with the under-sampling technique such as Cluster Centroids to solve the credit cards' fraud transactions detection problem. Experimental results show that that the graph p-Laplacian semi-supervised learning methods outperform the current state of art graph Laplacian based semi-supervised learning method (p=2).

Research paper thumbnail of Tensor sparse PCA and face recognition: a novel approach

SN Applied Sciences

Face recognition is the important field in machine learning and pattern recognition research area... more Face recognition is the important field in machine learning and pattern recognition research area. It has a lot of applications in military, finance, public security, to name a few. In this paper, the combination of the tensor sparse PCA with the nearest-neighbor method (and with the kernel ridge regression method) will be proposed and applied to the face dataset. Experimental results show that the combination of the tensor sparse PCA with any classification system does not always reach the best accuracy performance measures. However, the accuracy of the combination of the sparse PCA method and one specific classification system is always better than the accuracy of the combination of the PCA method and one specific classification system and is always better than the accuracy of the classification system itself.

Research paper thumbnail of Directed Hypergraph Neural Network

Journal of Advanced Research in Dynamical and Control Systems

To deal with irregular data structure, graph convolution neural networks have been developed by a... more To deal with irregular data structure, graph convolution neural networks have been developed by a lot of data scientists. However, data scientists just have concentrated primarily on developing deep neural network method for un-directed graph. In this paper, we will present the novel neural network method for directed hypergraph. In the other words, we will develop not only the novel directed hypergraph neural network method but also the novel directed hypergraph based semi-supervised learning method. These methods are employed to solve the node classification task. The two datasets that are used in the experiments are the cora and the citeseer datasets. Among the classic directed graph based semi-supervised learning method, the novel directed hypergraph based semisupervised learning method, the novel directed hypergraph neural network method that are utilized to solve this node classification task, we recognize that the novel directed hypergraph neural network achieves the highest accuracies.

Research paper thumbnail of Combinatorial and Random Walk Hypergraph Laplacian Eigenmaps

International Journal of Machine Learning and Computing, 2015

Most network-based machine learning methods are based on the assumption that the labels of two ad... more Most network-based machine learning methods are based on the assumption that the labels of two adjacent vertices in the network are likely to be the same. However, assuming the pairwise relationship between vertices is not complete. The information a group of vertices that show very similar patterns and tend to have similar labels is missed. The natural way overcoming the information loss of the above assumption is to represent the given data as the hypergraph. However, representing the dataset as the hypergraph will not lead to the perfection. The number of hyper-edges may be large; hence this will lead to high time complexity of the clustering methods or the classification methods when we try to apply the clustering/classification methods to this hypergraph dataset. Thus, there exists a need to develop the dimensional reduction methods for the hypergraph datasets. In this paper, the two un-normalized and random walk hypergraph Laplacian Eigenmaps are introduced. Experiment results show that the accuracy performance measures of these two hypergraph Laplacian Eigenmaps combined with graph based semi-supervised learning method are greater than the accuracy performance measure of graph based semi-supervised learning method alone (i.e. the baseline method of this paper) applied to the original hypergraph datasets.

Research paper thumbnail of Un-Normalized Graph P-Laplacian Semi-Supervised Learning Method Applied to Cancer Classification Problem

Journal of Automation and Control Engineering, 2015

Research paper thumbnail of Hypergraph and Protein Function Prediction with Gene Expression Data

Journal of Automation and Control Engineering, 2015

Research paper thumbnail of Kernel ridge regression method applied to speech recognition problem: A novel approach

2014 International Conference on Advanced Technologies for Communications (ATC 2014), 2014

Speech recognition is the important problem in pattern recognition research field. In this paper,... more Speech recognition is the important problem in pattern recognition research field. In this paper, the kernel ridge regression method is proposed to be applied to the MFCC feature vectors of the speech dataset available from IC Design lab at Faculty of Electricals-Electronics Engineering, University of Technology, Ho Chi Minh City. Experiment results show that the kernel ridge regression method outperforms the current state of the art Hidden Markov Model method in speech recognition problem in terms of sensitivity performance measure and calculation speed of training process.

Research paper thumbnail of The Un-normalized Graph p-Laplacian Based Semi-supervised Learning Method and Protein Function Prediction Problem

Advances in Intelligent Systems and Computing, 2014

ABSTRACT

Research paper thumbnail of Application of three graph Laplacian based semisupervised learning methods to protein function prediction problem

International Journal on Bioinformatics & Biosciences, 2013

Protein function prediction is the important problem in modern biology. In this paper, the un-nor... more Protein function prediction is the important problem in modern biology. In this paper, the un-normalized, symmetric normalized, and random walk graph Laplacian based semi-supervised learning methods will be applied to the integrated network combined from multiple networks to predict the functions of all yeast proteins in these multiple networks. These multiple networks are network created from Pfam domain structure, co-participation in a protein complex, protein-protein interaction network, genetic interaction network, and network created from cell cycle gene expression measurements. Multiple networks are combined with fixed weights instead of using convex optimization to determine the combination weights due to high time complexity of convex optimization method. This simple combination method will not affect the accuracy performance measures of the three semi-supervised learning methods. Experiment results show that the un-normalized and symmetric normalized graph Laplacian based methods perform slightly better than random walk graph Laplacian based method for integrated network. Moreover, the accuracy performance measures of these three semi-supervised learning methods for integrated network are much better than the best accuracy performance measures of these three methods for the individual network.