On GPU-CUDA as preprocessing of fuzzy-rough data reduction by means of singular value decomposition

Data reduction algorithms often produce inaccurate results for loss of relevant information. Recently, the singular value decomposition (SVD) method has been used as preprocessing method in order to deal with high-dimensional data and achieve fuzzy-rough reduct convergence on higher dimensional datasets. Despite the well-known fact that SVD offers attractive properties, its high computational cost remains a critical issue. In this work, we present a parallel implementation of the SVD algorithm on graphics processing units using CUDA programming model. Our approach is based on an iterative parallel version of the QR factorization by means of Givens rotations using the Sameh and Kuck scheme. Our results show significant improvements in terms of performances with respect to the CPU version that encourage its usability for this expensive processing of data.