Large Latent Semantic Indexing via a Semi-Discrete Matrix Decomposition

Abstract

With the electronic storage of documents comes the possibility of building search engines that can automatically choose documents relevant to a given set of topics. In information retrieval, we wish to match queries with relevant documents. Documents can be represented by the terms that appear within them, but literal matching of terms does not necessarily retrieve all relevant documents. There are a number of information retrieval systems based on inexact matches. Latent Semantic Indexing represents documents by approximations and tends to cluster documents on similar topics even if their term profiles are somewhat different. This approximate representation is usually accomplished using a low-rank singular value decomposition (SVD) approximation. In this paper, we use an alternate decomposition, the semi-discrete decomposition (SDD). For equal query times, the SDD does as well as the SVD and uses less than one-tenth the storage for the MEDLINE test set. (Also cross-referenced as UMIACS-TR-96-83)