Fuzzy Systems and Knowledge Discovery, Fourth International Conference on (2007)

Haikou, Hainan, China

Aug. 24, 2007 to Aug. 27, 2007

ISBN: 0-7695-2874-0

pp: 239-242

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FSKD.2007.383

Jiani Hu , Beijing University of Posts and Telecommunications

Weihong Deng , Beijing University of Posts and Telecommunications

Jun Guo , Beijing University of Posts and Telecommunications

Weiran Xu , Beijing University of Posts and Telecommunications

ABSTRACT

This paper introduces a locality discriminating indexing (LDI) algorithm for text categorization. The LDI algorithm offers a manifold way of discriminant analysis. Based on the hypothesis that samples from different classes reside in class-specific manifold structures, the algorithm depicts the manifold structures by a nearest-native graph and a invader graphs. And a new locality discriminant criterion is pro- posed, which best preserves the within-class local struc- tures while suppresses the between-class overlap. Using the notion of the Laplacian of the graphs, the LDI algo- rithm finds the optimal linear transformation by solving the generalized eigenvalue problem. The feasibility of the LDI algorithm has been successfully tested in text categorization using 20NG and Reuters-21578 databases. Experiment re- sults show LDI is an effective technique for document mod- eling and representations for classification.

INDEX TERMS

CITATION

J. Guo, W. Deng, W. Xu and J. Hu, "Learning Locality Discriminating Indexing for Text Categorization,"

*2007 International Conference on Fuzzy Systems and Knowledge Discovery(FSKD)*, Haikou, 2007, pp. 239-242.

doi:10.1109/FSKD.2007.383

CITATIONS