high performance computing on graphics processing units: hgpu.org

This paper proposes a partial parallelization for the Successive Projections Algorithm (SPA), which is a variable selection technique designed for use with Multiple Linear Regression. This implementation is aimed at improving the computational efficiency of SPA, without changing the outcome of the algorithm. For this purpose, a new strategy of inverse matrix calculation is employed. The advantage of the proposed implementation is demonstrated in an example involving large matrixes. In this example, gains of speedup were obtained.