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Neural Computing and Applications
Published in: Neural Computing and Applications 1/2017

05-05-2016 | Original Article

Collaborative representation analysis methods for feature extraction

Authors: Juliang Hua, Huan Wang, Mingu Ren, Heyan Huang

Published in: Neural Computing and Applications | Special Issue 1/2017

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Abstract

Recently, sparse representation (SR) theory gets much success in the fields of pattern recognition and machine learning. Many researchers use SR to design classification methods and dictionary learning via reconstruction residual. It was shown that collaborative representation (CR) is the key part in sparse representation-based classification (SRC) and collaborative representation-based classification (CRC). Both SRC and CRC are good classification methods. Here, we give a collaborative representation analysis (CRA) method for feature extraction. Not like SRC-/CRC-based methods (e.g., SPP and CRP), CRA could directly extract the features like PCA and LDA. Further, a Kernel CRA (KCRA) is developed via kernel tricks. The experimental results on FERET and AR face databases show that CRA and KCRA are two effective feature extraction methods and could get good performance.
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Metadata
Title
Collaborative representation analysis methods for feature extraction
Authors
Juliang Hua
Huan Wang
Mingu Ren
Heyan Huang
Publication date
05-05-2016
Publisher
Springer London
Published in
Neural Computing and Applications / Issue Special Issue 1/2017
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2299-3

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