Multistep Sparse Approximation Technology in Information Retrieval

With large sets of text documents increasing rapidly, being able to efficiently utilize this vast volume of new information and ser-vice resource presents challenges to computational scientists. Text documents are usually modeled as a term-document matrix which has high dimensional and space vectors. To reduce the high di-mensions, one of the various dimensionality reduction methods, concept decomposition, has been developed by some researchers. This method is based on document clustering techniques and least-square matrix approximation to approximate the matrix of vectors. However the numerical computation is expensive, as an inverse of a dense matrix formed by the concept vector matrix is required. In this paper we presented a class of multistep spare matrix strategies for concept decomposition matrix approximation. In this approach, a series of simple sparse matrices are used to approximate the de-compositions. Our numerical experiments on both small and large datasets show the advantage of such an approach in terms of stor-age costs and query time compared with the least-squares based approach while maintaining comparable retrieval quality