Journal
KNOWLEDGE-BASED SYSTEMS
Volume 37, Issue -, Pages 388-393Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.knosys.2012.08.017
Keywords
Multiple kernel learning; Empirical mapping; Random projection; Ensemble classifier; Pattern recognition
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Funding
- Natural Science Foundations of China [60903091, 61170151, 61272198, 21176077]
- Specialized Research Fund for the Doctoral Program of Higher Education [20090074120003]
- Fundamental Research Funds for the Central Universities
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In this paper we propose an effective and efficient random projection ensemble classifier with multiple empirical kernels. For the proposed classifier, we first randomly select a subset from the whole training set and use the subset to construct multiple kernel matrices with different kernels. Then through adopting the eigendecomposition of each kernel matrix, we explicitly map each sample into a feature space and apply the transformed sample into our previous multiple kernel learning framework. Finally, we repeat the above random selection for multiple times and develop a voting ensemble classifier, which is named RPEMEKL. The contributions of the proposed RPEMEKL are: (1) efficiently reducing the computational cost for the eigendecomposition of the kernel matrix due to the smaller size of the kernel matrix; (2) effectively increasing the classification performance due to the diversity generated through different random selections of the subsets: (3) giving an alternative multiple kernel learning from the Empirical Kernel Mapping (EKM) viewpoint, which is different from the traditional Implicit Kernel Mapping (IKM) learning. (C) 2012 Elsevier B.V. All rights reserved.
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