Abstract
In this paper, we propose a new method of extracting the discriminative features for classification from a given training dataset. The proposed method combines the advantages of both the null space method and the maximum margin criterion (MMC) method, whilst overcomes their drawbacks. The better performance of the proposed method is confirmed by face recognition experiments.
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Zheng, W., Zou, C., Zhao, L. (2005). Discriminative Features Extraction in Minor Component Subspace. In: Tao, J., Tan, T., Picard, R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2005. Lecture Notes in Computer Science, vol 3784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573548_28
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DOI: https://doi.org/10.1007/11573548_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29621-8
Online ISBN: 978-3-540-32273-3
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