A new approach for the parallel computation of singular value decomposition (SVD) of matrix A 2 C n is proposed. Contrary to the known algorithms that use a static cyclic ordering of subproblems… (More)

One way, how to speed up the computation of the singular value decomposition of a given matrix A ∈ C, m ≥ n, by the parallel two-sided block-Jacobi method, consists of applying some pre-processing… (More)

An alternative to singular value decomposition (SVD) in the information retrieval is the low-rank approximation of an original non-negative matrix A by its non-negative factors U and V . The columns… (More)

In latent semantic indexing, the addition of documents (or the addition of terms) to some already processed text collection leads to the updating of the best rank-k approximation of the term-document… (More)

One sided block Jacobi algorithm for the singular value decomposition (SVD) of matrix can be a method of choice to compute SVD efficiently and accurately in parallel. A given matrix is logically… (More)

Dimensionality reduction is an established area in text mining and information retrieval. These methods convert the highly sparse corpus matrices into dense matrix format while preserving or… (More)

Dimensionality reduction by algebraic methods is an established technique to address a number of problems in information retrieval. These methods are known to alleviate synonymy and polysemy, but… (More)

Nonnegative Matrix Factorization (NMF) is a technique to approximate a nonnegative matrix as a product of two smaller nonnegative matrices. The guaranteed nonnegativity of the factors allows… (More)