Abstract
Abstract We investigate a methodology for matrix approximation andIR. A central feature of these techniques is an initial clus-tering phase on the columns of the term-document matrix,followed by partial SVD on the columns constituting eachcluster. The extracted information is used to build effectivelow rank approximations to the original matrix as well as forIR. The algorithms can be expressed by means of rank reduc-tion formulas. Experiments indicate that these methods canachieve good overall performance for matrix approximationand IR and compete well with existing schemes. Keywords : Low rank approximations, Clustering, LSI. 1 Introduction and motivation The purpose of this paper is to outline aspects of a frame-work for matrix approximation and its application in LSI 1 .This framework is designed in the context of the vector spacemodel [18], where a collection of n documents is repre-sented by a term-document matrix (abbreviated as tdm) ofncolumns and mrows, where mis the number of terms (orphrases) used to index the collection. Each element fi
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