A fast approximate algorithm for large-scale Latent Semantic Indexing

Abstract

Latent semantic indexing (LSI) is an effective method to discover the underlying semantic structure of data. It has numerous applications in information retrieval and data mining. However, the computational complexity of LSI may be prohibitively high when applied to very large datasets. In this paper, we present a fast approximate algorithm for large-scale LSI that is conceptually simple and theoretically justified. Our main contribution is to show that the proposed algorithm has provable error bound and linear computational complexity.