Hypergraph-based multilevel matrix approximation for text information retrieval

In Latent Semantic Indexing (LSI), a collection of documents is often pre-processed to form a sparse term-document matrix, followed by a computation of a low-rank approximation to the data matrix. A multilevel framework based on hypergraph coarsening is presented which exploits the hypergraph that is canonically associated with the sparse term-document matrix representing the data. The main goal is to reduce the cost of the matrix approximation without sacrificing accuracy. Because coarsening by multilevel hypergraph techniques is a form of clustering, the proposed approach can be regarded as a hybrid of factorization-based LSI and clustering-based LSI. Experimental results indicate that our method achieves good improvement of the retrieval performance at a reduced cost. Categories and Subject Descriptors H.3.1 [Content Analysis and Indexing]: Indexing methods; G.2.2 [Graph Theory]: Hypergraphs; F.2.1 [Numerical Algorithms and Problems]: Computations on matrices General Terms Algorit...

• Type: Journal
• Year: 2010
• Where: CIKM
• Authors: Haw-ren Fang, Yousef Saad
• Keywords: CIKM 2010, Hypergraph, Information Technology, Latent Semantic Indexing, Sparse Term-document Matrix