TY - JOUR

T1 - Enforcement of the principal component analysis–extreme learning machine algorithm by linear discriminant analysis

AU - Castaño, A.

AU - Fernández-Navarro, F.

AU - Riccardi, Annalisa

AU - Hervás-Martínez, C.

PY - 2016/8/1

Y1 - 2016/8/1

N2 - 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.

AB - 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.

KW - Extreme learning machine

KW - Linear discriminant analysis

KW - Neural networks

KW - Principal component analysis

UR - http://www.scopus.com/inward/record.url?scp=84933059504&partnerID=8YFLogxK

U2 - 10.1007/s00521-015-1974-0

DO - 10.1007/s00521-015-1974-0

M3 - Article

AN - SCOPUS:84933059504

SN - 0941-0643

VL - 27

SP - 1749

EP - 1760

JO - Neural Computing and Applications

JF - Neural Computing and Applications

IS - 6

ER -