Face recognition based on extreme learning machine

Extreme learning machine (ELM) is an efficient learning algorithm for generalized single hidden layer feedforward networks (SLFNs), which performs well in both regression and classification applications. It has recently been shown that from the optimization point of view ELM and support vector machi...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2011-09, Vol.74 (16), p.2541-2551
Main Authors: Zong, Weiwei, Huang, Guang-Bin
Format: Article
Language:English
Subjects:
Classification
Discriminative locality alignment
Elm
Extreme learning machine
Face recognition
Learning
Neural networks
OAO
One-against-all
One-against-one
Optimization
Regression
Support vector machine
Support vector machines
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Summary:Extreme learning machine (ELM) is an efficient learning algorithm for generalized single hidden layer feedforward networks (SLFNs), which performs well in both regression and classification applications. It has recently been shown that from the optimization point of view ELM and support vector machine (SVM) are equivalent but ELM has less stringent optimization constraints. Due to the mild optimization constraints ELM can be easy of implementation and usually obtains better generalization performance. In this paper we study the performance of the one-against-all (OAA) and one-against-one (OAO) ELM for classification in multi-label face recognition applications. The performance is verified through four benchmarking face image data sets.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2010.12.041