Latent Semantic Indexing: A Probabilistic Analysis
Christos H. Papadimitriou, Prabhakar Raghavan, Hisao Tamaki, Santosh Vempala
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Abstract
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.

References

References, where available, link to the DBLP on the World Wide Web.

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BIBTEX

@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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