Research Interests, Accomplishments, and Goals
نویسنده
چکیده
In text mining, a collection of documents is often pre-processed to form a sparse termdocument matrix. In Latent Semantic Indexing (LSI), this is followed by a computation of a low-rank approximation to the data matrix, in order to filter out noise and redundancy due to word usage. The computation of these low-rank approximation by factorization algorithms can be time-consuming when the data set is large. 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 of this technique 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 clusteringbased LSI. Experimental results indicate that the multilevel hypergraph technique achieves good improvement of the retrieval performance at a reduced cost. This is joint work with Y. Saad.
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