Murat Akcakaya - Academia.edu (original) (raw)
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Papers by Murat Akcakaya
IEEE Transactions on Signal Processing, 2015
Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp '14 Adjunct, 2014
IEEE Signal Processing Letters, 2015
2010 International Waveform Diversity and Design Conference, 2010
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2015
Brain-Computer Interfaces, 2014
Lecture Notes in Computer Science, 2013
2011 IEEE International Symposium on Antennas and Propagation (APSURSI), 2011
IEEE Signal Processing Letters, 2015
ABSTRACT We show that mode-based cluster boundaries exhibit themselves as minor surfaces of the d... more ABSTRACT We show that mode-based cluster boundaries exhibit themselves as minor surfaces of the data probability density function. Based on this result, we provide a connectivity measure depending on minor surface search between sample pairs. Accordingly, we build a connectivity graph among data samples. The use of graph construction is particularly demonstrated for clustering, but applications in other machine learning areas are possible. On Gaussian mixture and kernel density estimate type probability density models, we illustrate the theoretical results with examples and demonstrate that cluster boundaries between sample pairs can be detected using a line integral. We also demonstrate an example where the data distribution has a continuous line segment as its set of local maxima (not strict), for which mean-shift like gradient flow and other mode-seeking algorithms fail to identify a single cluster, while the proposed approach successfully determines this fact.
Conference Record - Asilomar Conference on Signals, Systems and Computers, 2008
Pattern Recognition Letters, 2014
Nonlinear dimensionality reduction is essential for the analysis and the interpretation of high d... more Nonlinear dimensionality reduction is essential for the analysis and the interpretation of high dimensional data sets. In this manuscript, we propose a distance order preserving manifold learning algorithm that extends the basic mean-squared error cost function used mainly in multidimensional scaling (MDS)-based methods. We develop a constrained optimization problem by assuming explicit constraints on the order of distances in the low-dimensional space. In this optimization problem, as a generalization of MDS, instead of forcing a linear relationship between the distances in the high-dimensional original and low-dimensional projection space, we learn a non-decreasing relation approximated by radial basis functions. We compare the proposed method with existing manifold learning algorithms using synthetic datasets based on the commonly used residual variance and proposed percentage of violated distance orders metrics. We also perform experiments on a retinal image dataset used in Retinopathy of Prematurity (ROP) diagnosis.
The Journal of the Acoustical Society of America, 2008
IEEE Transactions on Signal Processing, 2000
IEEE Transactions on Signal Processing, 2000
IEEE Transactions on Aerospace and Electronic Systems, 2000
IEEE Signal Processing Letters, 2000
ABSTRACT Accurate multimodal and multisensor detection of a target phenomenon requires knowledge ... more ABSTRACT Accurate multimodal and multisensor detection of a target phenomenon requires knowledge of probabilistic sensor characteristics to determine an appropriate fusion rule which optimizes an objective of interest, traditionally the expected Bayesian risk. However, a particular sensor characteristic can change online, introducing unaccounted additional risk to the fusion rule that was based on assumed sensor specifications. To mitigate such changes, we propose a sensor-failure-robust fusion rule assuming that only first order characteristics of a probabilistic sensor failure model are known. Under this failure model, we compute the expected Bayesian risk and minimize this risk to develop the proposed fusion method.
IEEE Transactions on Signal Processing, 2015
Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp '14 Adjunct, 2014
IEEE Signal Processing Letters, 2015
2010 International Waveform Diversity and Design Conference, 2010
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2015
Brain-Computer Interfaces, 2014
Lecture Notes in Computer Science, 2013
2011 IEEE International Symposium on Antennas and Propagation (APSURSI), 2011
IEEE Signal Processing Letters, 2015
ABSTRACT We show that mode-based cluster boundaries exhibit themselves as minor surfaces of the d... more ABSTRACT We show that mode-based cluster boundaries exhibit themselves as minor surfaces of the data probability density function. Based on this result, we provide a connectivity measure depending on minor surface search between sample pairs. Accordingly, we build a connectivity graph among data samples. The use of graph construction is particularly demonstrated for clustering, but applications in other machine learning areas are possible. On Gaussian mixture and kernel density estimate type probability density models, we illustrate the theoretical results with examples and demonstrate that cluster boundaries between sample pairs can be detected using a line integral. We also demonstrate an example where the data distribution has a continuous line segment as its set of local maxima (not strict), for which mean-shift like gradient flow and other mode-seeking algorithms fail to identify a single cluster, while the proposed approach successfully determines this fact.
Conference Record - Asilomar Conference on Signals, Systems and Computers, 2008
Pattern Recognition Letters, 2014
Nonlinear dimensionality reduction is essential for the analysis and the interpretation of high d... more Nonlinear dimensionality reduction is essential for the analysis and the interpretation of high dimensional data sets. In this manuscript, we propose a distance order preserving manifold learning algorithm that extends the basic mean-squared error cost function used mainly in multidimensional scaling (MDS)-based methods. We develop a constrained optimization problem by assuming explicit constraints on the order of distances in the low-dimensional space. In this optimization problem, as a generalization of MDS, instead of forcing a linear relationship between the distances in the high-dimensional original and low-dimensional projection space, we learn a non-decreasing relation approximated by radial basis functions. We compare the proposed method with existing manifold learning algorithms using synthetic datasets based on the commonly used residual variance and proposed percentage of violated distance orders metrics. We also perform experiments on a retinal image dataset used in Retinopathy of Prematurity (ROP) diagnosis.
The Journal of the Acoustical Society of America, 2008
IEEE Transactions on Signal Processing, 2000
IEEE Transactions on Signal Processing, 2000
IEEE Transactions on Aerospace and Electronic Systems, 2000
IEEE Signal Processing Letters, 2000
ABSTRACT Accurate multimodal and multisensor detection of a target phenomenon requires knowledge ... more ABSTRACT Accurate multimodal and multisensor detection of a target phenomenon requires knowledge of probabilistic sensor characteristics to determine an appropriate fusion rule which optimizes an objective of interest, traditionally the expected Bayesian risk. However, a particular sensor characteristic can change online, introducing unaccounted additional risk to the fusion rule that was based on assumed sensor specifications. To mitigate such changes, we propose a sensor-failure-robust fusion rule assuming that only first order characteristics of a probabilistic sensor failure model are known. Under this failure model, we compute the expected Bayesian risk and minimize this risk to develop the proposed fusion method.