Kernel dictionary learning based discriminant analysis |
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| Affiliation: | 1. College of Engineering and Science, Victoria University, VIC, Australia;2. Centre for Applied Informatics (CAI), Victoria University, VIC, Australia;1. Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China;2. School of Computer, Shenyang Aerospace University, Liaoning 110136, China;3. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China;1. Nanjing University of Information Science Technology (NUIST), Nanjing, China;2. School of Communication and Information Engineering, Shanghai University, Shanghai, China;3. Key Laboratory of Specialty Fiber Optics and Optical Access, Shanghai University, Shanghai, China;1. Computer Application Research Center, ShenZhen Graduate School, Harbin Institute of Technology, ShenZhen, China;2. School of Electronic Information Engineering, Tianjin University, Tianjin, China;3. School of Computing, National University of Singapore, Singapore |
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| Abstract: | Sparse representation based classification (SRC) has been successfully applied in many applications. But how to determine appropriate features that can best work with SRC remains an open question. Dictionary learning (DL) has played an import role in the success of sparse representation, while SRC treats the entire training set as a structured dictionary. In addition, as a linear algorithm, SRC cannot handle the data with highly nonlinear distribution. Motivated by these concerns, in this paper, we propose a novel feature learning method (termed kernel dictionary learning based discriminant analysis, KDL-DA). The proposed algorithm aims at learning a projection matrix and a kernel dictionary simultaneously such that in the reduced space the sparse representation of the data can be easily obtained, and the reconstruction residual can be further reduced. Thus, KDL-DA can achieve better performances in the projected space. Extensive experimental results show that our method outperforms many state-of-the-art methods. |
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| Keywords: | Kernel method Sparse representation Dictionary learning Feature learning |
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