Sparse representations for text categorization
Tara N. Sainath, Sameer Maskey, Dimitri Kanevsky, Bhuvana Ramabhadran, D. Nahamoo, Julia Hirschberg · 2010
Sparse representations (SRs) are often used to characterize a test signal using few support training examples, and allow the number of supports to be adapted to the specific signal being categorized. Given the good performance of SRs compared to other classifiers for both image classification and phonetic clas-sification, in this paper, we extended the use of SRs for text classification, a method which has thus far not been explored for this domain. Specifically, we demonstrate how sparse repre-sentations can be used for text classification and how their per-formance varies with the vocabulary size of the documents. In addition, we also show that this method offers promising results over the Naive Bayes (NB) classifier, a standard baseline classi-fier used for text categorization, thus introducing an alternative class of methods for text categorization. 1.