Bilinear analysis for Kernel selection and nonlinear feature extraction
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AbstractThis paper presents a unified criterion, Fisher + kernel criterion (FKC), for feature extraction and recognition. This new criterion is intended to extract the most discriminant features in different nonlinear spaces, and then, fuse these features under a unified measurement. Thus, FKC can simultaneously achieve nonlinear discriminant analysis and kernel selection. In addition, we present an efficient algorithm Fisher + kernel analysis (FKA), which utilizes the bilinear analysis, to optimize the new criterion. This FKA algorithm can alleviate the ill-posed problem existed in traditional kernel discriminant analysis (KDA), and usually, has no singularity problem. The effectiveness of our proposed algorithm is validated by a series of face-recognition experiments on several different databases.
All Author(s) ListYang S, Yan S, Zhang C, Tang X
Journal nameIEEE Transactions on Neural Networks
Volume Number18
Issue Number5
Pages1442 - 1452
LanguagesEnglish-United Kingdom
Keywordsbilinear analysis; discriminant analysis; face recognition; feature extraction; Fisher criterion; kernel selection
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering; Engineering, Electrical & Electronic

Last updated on 2020-08-08 at 01:21