Fuzzy Systems and Knowledge Discovery, Fourth International Conference on (2007)
Haikou, Hainan, China
Aug. 24, 2007 to Aug. 27, 2007
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
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.
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.