Title Enforcement of the principal component analysis-extreme learning machine algorithm by linear discriminant analysis
Authors Castano, A., FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, Riccardi, Annalisa, Hervas-Martinez, C., FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS
External publication No
Means Neural Comput. Appl.
Scope Article
Nature Científica
JCR Quartile 2
SJR Quartile 2
JCR Impact 2.50500
SJR Impact 0.60200
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84933059504&doi=10.1007%2fs00521-015-1974-0&partnerID=40&md5=4f915bb673feb4b8019e52be6e776fd5
Publication date 01/08/2016
ISI 000379079600024
Scopus Id 2-s2.0-84933059504
DOI 10.1007/s00521-015-1974-0
Abstract In the majority of traditional extreme learning machine (ELM) approaches, the parameters of the basis functions are randomly generated and do not need to be tuned, while the weights connecting the hidden layer to the output layer are analytically estimated. The determination of the optimal number of basis functions to be included in the hidden layer is still an open problem. Cross-validation and heuristic approaches (constructive and destructive) are some of the methodologies used to perform this task. Recently, a deterministic algorithm based on the principal component analysis (PCA) and ELM has been proposed to assess the number of basis functions according to the number of principal components necessary to explain the 90 % of the variance in the data. In this work, the PCA part of the PCA-ELM algorithm is joined to the linear discriminant analysis (LDA) as a hybrid means to perform the pruning of the hidden nodes. This is justified by the fact that the LDA approach is outperforming the PCA one on a set of problems. Hence, the idea of combining the two approaches in a LDA-PCA-ELM algorithm is shown to be in average better than its PCA-ELM and LDA-ELM counterparts. Moreover, the performance in classification and the number of basis functions selected by the algorithm, on a set of benchmark problems, have been compared and validated in the experimental section using nonparametric tests against a set of existing ELM techniques.
Keywords Principal component analysis; Linear discriminant analysis; Extreme learning machine; Neural networks
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