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Sparse extreme learning machine classifier exploiting intrinsic graphs

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)192-196
Number of pages5
JournalPattern Recognition Letters
Volume65
DOIs
Publication statusPublished - 1 Nov 2015
Publication typeA1 Journal article-refereed

Abstract

This paper presents an analysis of the recently proposed sparse extreme learning machine (S-ELM) classifier and describes an optimization scheme that can be used to calculate the network output weights. This optimization scheme exploits intrinsic graph structures in order to describe geometric data relationships in the so-called ELM space. Kernel formulations of the approach operating in ELM spaces of arbitrary dimensions are also provided. It is shown that the application of the optimization scheme exploiting geometric data relationships in the original ELM space is equivalent to the application of the original S-ELM to a transformed ELM space. The experimental results show that the incorporation of geometric data relationships in S-ELM can lead to enhanced performance.

Keywords

  • Intrinsic graphs, Single-hidden layer neural networks, Sparse extreme learning machine

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