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Neural Computing and Applications

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05.05.2016 | Original Article

Collaborative representation analysis methods for feature extraction

verfasst von: Juliang Hua, Huan Wang, Mingu Ren, Heyan Huang

Erschienen in: Neural Computing and Applications | Sonderheft 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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Titel: Collaborative representation analysis methods for feature extraction
verfasst von: Juliang Hua
Huan Wang
Mingu Ren
Heyan Huang
Publikationsdatum: 05.05.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 1/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2299-3

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