### 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.

Original language | English |
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Pages (from-to) | 1749-1760 |

Number of pages | 12 |

Journal | Neural Computing and Applications |

Volume | 27 |

Issue number | 6 |

Early online date | 25 Jun 2015 |

DOIs | |

Publication status | Published - 31 Aug 2016 |

### Keywords

- principal component analysis
- linear discriminant analysis
- extreme learning machine
- neural networks

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## Cite this

Castaño, A., Fernández-Navarro, F., Riccardi, A., & Hervás-Martínez, C. (2016). Enforcement of the principal component analysis - extreme learning machine algorithm by linear discriminant analysis.

*Neural Computing and Applications*,*27*(6), 1749-1760. https://doi.org/10.1007/s00521-015-1974-0