2008 FastComputationofMoorePenroseIn

• (Courrieu, 2008) ⇒ Pierre Courrieu. (2008). “Fast Computation of Moore-Penrose Inverse Matrices .” In: Neural Information Processing - Letters and Reviews Journal, 8.

Subject Headings: Pseudo-Inverse Algorithm; Pseudo-Inverse Matrix.

Cited By

• Google Scholar: 206 Citations [1]
• Semantic Scholar: 80 Citations [2]

Quotes

Author Keywords

• Rank Deficient Least Square Systems; Moore-Penrose Inverse; Pseudoinverse; Generalized Inverse; Neural Learning; Minimum-norm Synaptic Weight Vectors; Regularization.

Abstract

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.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 FastComputationofMoorePenroseIn	Pierre Courrieu			Fast Computation of Moore-Penrose Inverse Matrices						2008