Text Classification Using Hierarchical Sparse Representation Classifiers (original) (raw)

2017

Abstract

In this paper, we propose to use sparse representation classifier (SRC) for text classification. The sparse representation of an example is obtained by using an overcomplete dictionary made up of term frequency (TF) vectors corresponding to all the training documents. We propose to seed the dictionary using principal components of TF vector representation corresponding to training text documents. In this work, we also propose 2-level hierarchical SRC (HSRC) by exploiting the similarity among the classes. We propose to use weighted decomposition principal component analysis (WDPCA) in the second level of HSRC to seed the dictionary to discriminate the similar classes. The effectiveness of the proposed approach to build HSRC for text classification is demonstrated on 20 Newsgroup Corpus.

Veena Thenkanidiyoor hasn't uploaded this paper.

Let Veena know you want this paper to be uploaded.

Ask for this paper to be uploaded.