Polynomial filtering in latent semantic indexing for information retrieval

Authors:
E. Kokiopoulou;Y. Saad
Affiliations:
University of Minnesota;University of Minnesota
Venue:
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2004

Citing 5
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Abstract

Latent Semantic Indexing (LSI) is a well established and effective framework for conceptual information retrieval. In traditional implementations of LSI the semantic structure of the collection is projected into the k-dimensional space derived from a rank-k approximation of the original term-by-document matrix. This paper discusses a new way to implement the LSI methodology, based on polynomial filtering. The new framework does not rely on any matrix decomposition and therefore its computational cost and storage requirements are low relative to traditional implementations of LSI. Additionally, it can be used as an effective information filtering technique when updating LSI models based on user feedback.