A differential LSI method for document classification

ABSTRACT

We have developed an effective probabilistic classifier for document classification by introducing the concept of the differential document vectors and DLSI (differential latent semantics index) spaces. A simple posteriori calculation using the intra- and extra-document statistics demonstrates the advantage of the DLSI space-based probabilistic classifier over the popularly used LSI space-based classifier in classification performance.

REFERENCES

• 1: M. Benkhalifa, A. Bensaid, and A Mouradi. 1999. Text categorization using the semi-supervised fuzzy c-means algorithm. In 18th International Conference of the North American Fuzzy Information Processing Society, pages 561--565.
• 2: Michael W. Berry , Susan T. Dumais , Gavin W. O'Brien, Using linear algebra for intelligent information retrieval, SIAM Review, v.37 n.4, p.573-595, Dec. 1995 [doi>10.1137/1037127]
• 3: Michael W. Berry , Zlatko Drmac , Elizabeth R. Jessup, Matrices, Vector Spaces, and Information Retrieval, SIAM Review, v.41 n.2, p.335-362, June 1999 [doi>10.1137/S0036144598347035]
• 4: Scott Deerwester, Susan T. Dumais, Grorge W. Furnas, Thomas K. Landauer, and Richard Harshman. 1990. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6):391--407.
• 5: Jennifer Farkas. 1994. Generating document clusters using thesauri and neural networks. In Canadian Conference on Electrical and Computer Engineering, volume 2, pages 710--713.
• 6: H. Hyotyniemi. 1996. Text document classification with self-organizing maps. In STeP '96 - Genes, Nets and Symbols. Finnish Artificial Intelligence Conference, pages 64--72.
• 7: M. Iwayama and T. Tokunaga. 1995. Hierarchical bayesian clustering for automatic text classification. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, volume 2, pages 1322--1327.
• 8: Wai Lam and Kon-Fan Low. 1997. Automatic document classification based on probabilistic reasoning: Model and performance analysis. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, volume 3, pages 2719--2723.
• 9: Dik L. Lee , Huei Chuang , Kent Seamons, Document Ranking and the Vector-Space Model, IEEE Software, v.14 n.2, p.67-75, March 1997 [doi>10.1109/52.582976 ]
• 10: Wei Li, Bob Lee, Franl Krausz, and Kenan Sahin. 1991. Text classification by a neural network. In Proceedings of the Twenty-Third Annual Summer Computer Simulation Conference, pages 313--318.
• 11: Michael L. Littman , Fan Jiang , Greg A. Keim, Learning a Language-Independent Representation for Terms from a Partially Aligned Corpus, Proceedings of the Fifteenth International Conference on Machine Learning, p.314-322, July 24-27, 1998
• 12: H. P. Luhn. 1958. The automatic creation of literature abstracts. IBM Journal of Research and Development, 2(2):159--165, April.
• 13: D. Merkl. 1998. Text classification with self-organizing maps: Some lessons learned. Neurocomputing, 21(1-3):61--77.
• 14: Baback Moghaddam , Alex Pentland, Probabilistic Visual Learning for Object Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.19 n.7, p.696-710, July 1997 [doi>10.1109/34.598227 ]
• 15: B. Moghaddam , W. Wahid , A. Pentland, Beyond Eigenfaces: Probabilistic Matching for Face Recognition, Proceedings of the 3rd. International Conference on Face & Gesture Recognition, p.30, April 14-16, 1998
• 16: Kamal Nigam , Andrew Kachites McCallum , Sebastian Thrun , Tom Mitchell, Text Classification from Labeled and Unlabeled Documents using EM, Machine Learning, v.39 n.2-3, p.103-134, May-June 2000
• 17: V. V. Raghavan and S. K. M. Wong. 1986. A critical analysis of vector space model for information retrieval. Journal of the American Society for Information Science, 37(5):279--87.
• 18: Gerard Salton , Michael J. McGill, Introduction to Modern Information Retrieval, McGraw-Hill, Inc., New York, NY, 1986
• 19: Gerard Salton , Christopher Buckley, Term-weighting approaches in automatic text retrieval, Information Processing and Management: an International Journal, v.24 n.5, p.513-523, 1988 [doi>10.1016/0306-4573(88)90021-0 ]
• 20: Hinrich Schütze , Craig Silverstein, Projections for efficient document clustering, Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval, p.74-81, July 27-31, 1997, Philadelphia, Pennsylvania, United States
• 21: L. Sirovich and M. Kirby. 1987. Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America A, 4(3):519--524.
• 22: Borge Svingen. 1997. Using genetic programming for document classification. In John R. Koza, editor, Late Breaking Papers at the 1997 Genetic Programming Conference, pages 240--245, Stanford University, CA, USA, 13-16 July. Stanford Bookstore.
• 23: M. Turk and A. Pentland. 1991. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1):71--86.
• 24: C. J. Van Rijsbergen, Information Retrieval, Butterworth-Heinemann, Newton, MA, 1979