NDLI: Combinatorial PCA and SVM methods for feature selection in learning classifications (applications to text categorization)

Content Provider	IEEE Xplore Digital Library
Author	Anghelescu, A.V. Muchnik, I.B.
Copyright Year	2003
Description	Author affiliation: Dept. of Comput. Sci., Rutgers Univ., USA (Anghelescu, A.V.)
Abstract	We describe a purely combinatorial approach of obtaining meaningful representations of text data. More precisely, we describe two different methods that materialize this approach: we call them combinatorial principal component analysis (cPCA) and combinatorial support vector machines (cSVM). These names emphasise mathematical analogies between the well known PCA and SVM, on one hand, and our respective methods. For evaluating the selected spaces of features, we used the environment set for TREC 2002 and used a very common classifier: 1-nearest neighbour (1-NN). We compared the results obtained on the feature sets calculated by the procedures we described (cPCA and cSVM) with the results obtained on the original feature space. We showed that by selecting a feature space on average 50 times smaller than the original space, the performance of the classifier does not decrease by more than 2%.
Sponsorship	IEEE Boston Sect. IEEE Robotics & Automation Soc. IEEE Neural Network Soc. IEEE Syst. Man & Cybernetics Soc. INNS U.S. Air Force U.S. Army U.S. Navy DARPA
Starting Page	491
Ending Page	496
File Size	348534
Page Count	6
File Format	PDF
ISBN	0780379586
DOI	10.1109/KIMAS.2003.1245090
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-09-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Computer science Degradation USA Councils Text categorization Support vector machine classification Nonlinear filters Classification algorithms Application software Principal component analysis
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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