Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
A semidiscrete matrix decomposition for latent semantic indexing information retrieval
ACM Transactions on Information Systems (TOIS)
Multilevel hypergraph partitioning: applications in VLSI domain
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Hypergraph-Partitioning-Based Decomposition for Parallel Sparse-Matrix Vector Multiplication
IEEE Transactions on Parallel and Distributed Systems
Algorithm 805: computation and uses of the semidiscrete matrix decomposition
ACM Transactions on Mathematical Software (TOMS)
Concept decompositions for large sparse text data using clustering
Machine Learning
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
Parallel hypergraph partitioning for scientific computing
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
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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