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On the kernel Extreme Learning Machine classifier

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)11-17
Number of pages7
JournalPattern Recognition Letters
Volume54
DOIs
Publication statusPublished - 1 Mar 2015
Publication typeA1 Journal article-refereed

Abstract

In this paper, we discuss the connection of the kernel versions of the ELM classifier with infinite Single-hidden Layer Feedforward Neural networks and show that the original ELM kernel definition can be adopted for the calculation of the ELM kernel matrix for two of the most common activation functions, i.e., the RBF and the sigmoid functions. In addition, we show that a low-rank decomposition of the kernel matrix defined on the input training data can be exploited in order to determine an appropriate ELM space for input data mapping. The ELM space determined from this process can be subsequently used for network training using the original ELM formulation. Experimental results denote that the adoption of the low-rank decomposition-based ELM space determination leads to enhanced performance, when compared to the standard choice, i.e., random input weights generation.

Keywords

  • Extreme learning machine, Infinite networks, Single-hidden layer networks