Volume 6 Number 7 (Jul. 2011)
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JSW 2011 Vol.6(7): 1265-1272 ISSN: 1796-217X
doi: 10.4304/jsw.6.7.1265-1272

An Efficient Discriminant Analysis Algorithm for Document Classification

Ziqiang Wang, Xia Sun
Henan University of Technology,Zhengzhou,China

Abstract—Document categorization has become one of the most important research areas of pattern recognition and data mining due to the exponential growth of documents in the Internet and the emergent need to organize them. The document space is always of very high dimensionality and learning in such a high dimensional space is often impossible due to the curse of dimensionality. To cope with performance and accuracy problems with high dimensionality, a novel dimensionality reduction algorithm called IKDA is proposed in this paper. The proposed IKDA algorithm combines kernel-based learning techniques and direct iterative optimization procedure to deal with the nonlinearity of the document distribution. The proposed algorithm also effectively solves the so-called “small sample size” problem in document classification task. Extensive experimental results on two real world data sets demonstrate the effectiveness and efficiency of the proposed algorithm.

Index Terms—document classification, kernel discriminant analysis, dimensionality reduction, data mining


Cite: Ziqiang Wang, Xia Sun, "An Efficient Discriminant Analysis Algorithm for Document Classification," Journal of Software vol. 6, no. 7, pp. 1265-1272, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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