A Priori Sparsity Patterns for Parallel Sparse Approximate Inverse Preconditioners
SIAM Journal on Scientific Computing
Concept decompositions for large sparse text data using clustering
Machine Learning
MSP: A Class of Parallel Multistep Successive Sparse Approximate Inverse Preconditioning Strategies
SIAM Journal on Scientific Computing
Telcordia LSI Engine: Implementation and Scalability Issues
RIDE '01 Proceedings of the 11th International Workshop on research Issues in Data Engineering
Text retrieval using sparsified concept decomposition matrix
CIS'04 Proceedings of the First international conference on Computational and Information Science
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In information retrieval, text documents are usually modeled as a term-document matrix which has high dimensional and space vectors. To reduce the high dimensions, one of the various dimensionality reduction methods, concept decomposition, has been developed by [3]. This method is based on document clustering techniques and least-square matrix approximation to approximate the matrix of vectors. Gao and Zhang [4] have indicated that the retrieval accuracy from the concept decomposition can be comparable to that from Latent Semantic Indexing. However the numerical computation is expensive. 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 decompositions. Our numerical experiments show the advantage of such an approach in terms of storage costs and query time compared with other approaches while maintaining comparable retrieval quality.