Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many neural learning algorithms require to solve large least square systems in order to obtain synaptic ights. Moore-Penrose inverse matrices allow for solving such systems, even with rank deficiency, and they provide minimum-norm vectors of synaptic ights, 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. , an algorithm based on a full rank Cholesky factorization is proposed. The resulting pseudoinverse matrices are similar to those provided by other algorithms. Hover the computation time is substantially shorter, particularly for large systems.
['Pierre Courrieu']
I am writing a research paper on a idea Fast Computation of Moore-Penrose Inverse Matrices
[{"from": "human", "value": "I am writing a research paper on a idea Fast Computation of Moore-Penrose Inverse Matrices"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many neural learning algorithms require to solve large least square systems in order to obtain synaptic ights. Moore-Penrose inverse matrices allow for solving such systems, even with rank deficiency, and they provide minimum-norm vectors of synaptic ights, 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. , an algorithm based on a full rank Cholesky factorization is proposed. The resulting pseudoinverse matrices are similar to those provided by other algorithms. Hover the computation time is substantially shorter, particularly for large systems."}]