Abstract
This paper develops a nonparametric marginal Fisher analysis (NMFA) technique for dimensionality reduction of high dimensional data. According to the different distributions of the training data, two classification criterions are proposed. Based on the new classification criterions, the local mean vectors with most discriminative information are selected to construct the corresponding nonparametric scatter matrices. By discovering the local structure, NMFA seeks to find a projection that maximizes the minimum extra-class distance and minimizes the maximum intra-class distance among the samples of single class simultaneously. The proposed method is applied to face recognition and is examined using the ORL and AR face image databases. Experiments show that our proposed method consistently outperforms some state-of-the-art techniques.
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Xu, J., Yang, J. (2010). Nonparametric Marginal Fisher Analysis for Feature Extraction. In: Huang, DS., Zhang, X., Reyes GarcÃa, C.A., Zhang, L. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2010. Lecture Notes in Computer Science(), vol 6216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14932-0_28
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DOI: https://doi.org/10.1007/978-3-642-14932-0_28
Publisher Name: Springer, Berlin, Heidelberg
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