Graph Embedded Extreme Learning Machine

IEEE Transactions on Cybernetics
Alexandros IosifidisIoannis Pitas

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.

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Citations

Jul 9, 2016·Neural Networks : the Official Journal of the International Neural Network Society·Jiuwen CaoXiaoping Lai
Apr 15, 2016·IEEE Transactions on Cybernetics· Xiao-Lin Xu Hong Yan
Apr 20, 2018·PloS One·Musatafa Abbas Abbood AlbadrMahmoud A M Sammour
Jan 24, 2017·IEEE Transactions on Cybernetics·Alexandros Iosifidis, Moncef Gabbouj

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