Parallel methods for linear systems solution in extreme learning machines: an overview
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Fecha
2020
Autores
Gelvez-Almeida, E
Baldera-Moreno, Y
Huérfano, Y
Vera, M
Mora, M
Barrientos, R
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Resumen
This paper aims to present an updated review of parallel algorithms for solving
square and rectangular single and double precision matrix linear systems using multi-core central
processing units and graphic processing units. A brief description of the methods for the solution
of linear systems based on operations, factorization and iterations was made. The methodology
implemented, in this article, is a documentary and it was based on the review of about 17
papers reported in the literature during the last five years (2016-2020). The disclosed findings
demonstrate the potential of parallelism to significantly decrease extreme learning machines
training times for problems with large amounts of data given the calculation of the Moore
Penrose pseudo inverse. The implementation of parallel algorithms in the calculation of the
pseudo-inverse will allow to contribute significantly in the applications of diversifying areas,
since it can accelerate the training time of the extreme learning machines with optimal results.
Descripción
Palabras clave
Multilayer perceptron, Support vector machines, Algorithms, Moore-Penrose
Citación
IOP Publishing