Approximate Kernel Extreme Learning Machine for Large Scale Data Classification

Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

Research output: Contribution to journalArticle (Academic Journal)peer-review

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Abstract

In this paper, we propose an approximation scheme of the Kernel Extreme Learning Machine algorithm for Single-hidden Layer Feedforward Neural network training that can be used for large scale classification problems. The Approximate Kernel Extreme Learning Machine is able to scale well in both computational cost and memory, while achieving good generalization performance. Regularized versions and extensions in order to exploit the total and within-class variance of the training data in the feature space are also proposed. Extensive experimental evaluation in medium-scale and large-scale classification problems denotes that the proposed approach is able to operate extremely fast in both the training and test phases and to provide satisfactory performance, outperforming relating classification schemes.
Original languageEnglish
Pages (from-to)210-220
Number of pages11
JournalNeurocomputing
Volume219
Early online date15 Sep 2016
DOIs
Publication statusPublished - 5 Jan 2017

Keywords

  • Extreme Learning Machine
  • Large Scale Learning
  • Facial Image Classification

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