Essence of kernel Fisher discriminant: KPCA plus LDA
نویسندگان
چکیده
In this paper, the method of kernel Fisher discriminant (KFD) is analyzed and its nature is revealed, i.e., KFD is equivalent to kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). Based on this result, a more transparent KFD algorithm is proposed. That is, KPCA is ;rst performed and then LDA is used for a second feature extraction in the KPCA-transformed space. Finally, the e=ectiveness of the proposed algorithm is veri;ed using the CENPARMI handwritten numeral database. ? 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
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ورودعنوان ژورنال:
- Pattern Recognition
دوره 37 شماره
صفحات -
تاریخ انتشار 2004