An Improved Kernel Principal Component Analysis Based on Chaotic Artificial Fish School Algorithm

Abstract:

The feature extraction is a great important step for face recognition. When all features are extracted and selected for face recognition, it results in poor recognition rate because there are too many irrelevant, redundant and noisy features which also increase the time consumption. Therefore, a good feature selection method is necessary. This problem can be regarded as a combinatorial optimization solution. To overcome this problem, An improved kernel principal component analysis based on chaotic artificial fish school algorithm is proposed. The feature subspace of face pictures is obtained by standard kernel principal component analysis where a better feature subspace is selected by improved chaotic artificial fish school algorithm which based on couple chaotic maps increases the diversity of fish, has better global convergence ability and is not easy to fall into local optimum when facing with complex problems. The experimental results show that the proposed method has significantly improved the performance of conventional kernel principal component analysis.

• Periodical: Advanced Materials Research (Volumes 655-657)
• Pages: 931-935
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.655-657.931
• Online since: January 2013
• Authors: Fang Min Hu*, Hui Ya Zhao
• Keywords: Artificial Fish School Algorithm, Chaos, Face Recognition, Feature Selection, Kernel Principal Component Analysis

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