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Graph Embedded Extreme Learning Machine

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)311 - 324
JournalIEEE Transactions on Cybernetics
Volume46
Issue number1
DOIs
Publication statusPublished - 2016
Externally publishedYes
Publication typeA1 Journal article-refereed

Abstract

In this paper, we propose a novel extension of the extreme learning machine (ELM) algorithm for single-hidden layer feedforward neural network training that is able to incorporate subspace learning (SL) criteria on the optimization process followed for the calculation of the network's output weights. The proposed graph embedded ELM (GEELM) algorithm is able to naturally exploit both intrinsic and penalty SL criteria that have been (or will be) designed under the graph embedding framework. In addition, we extend the proposed GEELM algorithm in order to be able to exploit SL criteria in arbitrary (even infinite) dimensional ELM spaces. We evaluate the proposed approach on eight standard classification problems and nine publicly available datasets designed for three problems related to human behavior analysis, i.e., the recognition of human face, facial expression, and activity. Experimental results denote the effectiveness of the proposed approach, since it outperforms other ELM-based classification schemes in all the cases.