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Fast Computation of Moore-Penrose Inverse Matrices by Courrieu, Pierre is a scholarly article available to read on EtoBox.

What is Fast Computation of Moore-Penrose Inverse Matrices about?

Many neural learning algorithms require to solve large least square systems in order to obtain synaptic weights. Moore-Penrose inverse matrices allow for solving such systems, even with rank deficiency, and they provide minimum-norm vectors of synaptic weights, which contribute to the regularization of the input-output mapping. It is thus of interest to develop fast and accurate algorithms for computing Moore-Penrose inverse matrices. In this paper, an algorithm based on a full rank Cholesky factorization is proposed. The resulting pseudoinverse matrices are similar to those provided by other algorithms. However the computation time is substantially shorter, particularly for large systems.

Author

Courrieu, Pierre

Published

2008

Language

EN