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Latent Semantic Indexing: A Probabilistic Analysis | Full Paper (PDF)
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Latent semantic indexing (LSI) is an information retrieval technique based on the spectral analysis of the term-document matrix, whose empirical success had heretofore been without rigorous prediction and explanation. We prove that, under certain conditions, LSI does succeed in capturing the underlying semantics of the corpus and achieves improved retrieval performance. We also propose the technique of random projection as a way of speeding up LSI. We complement our theorems with encouraging experimental results. We also argue that our results may be viewed in a more general framework, as a theoretical basis for the use of spectral methods in a wider class of applications such as collaborative filtering. |
@inproceedings{DBLP:conf/pods/PapadimitriouRTV98, author = {Christos H. Papadimitriou and Prabhakar Raghavan and Hisao Tamaki and Santosh Vempala}, title = {Latent Semantic Indexing: A Probabilistic Analysis}, booktitle = {Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, June 1-3, 1998, Seattle, Washington}, publisher = {ACM Press}, year = {1998}, isbn = {0-89791-966-3}, pages = {159-168}, crossref = {DBLP:conf/pods/98}, bibsource = {DBLP, http://dblp.uni-trier.de} }
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DBLP: Copyright ©1999 by Michael Ley (ley@uni-trier.de).
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