CLSI: A Flexible Approximation Scheme from Clustered Term-Document Matrices

We investigate a methodology for matrix approximation and IR. A central feature of these techniques is an initial clustering phase on the columns of the term-document matrix, followed by partial svd on the columns constituting each cluster. The extracted information is used to build effective low rank approximations to the original matrix as well as for IR. The algorithms can be expressed by means of rank reduction formulas. Experiments indicate that these methods can achieve good overall performance for matrix approximation and IR and compete well with existing schemes.