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A Primal Framework for Indefinite Kernel Learning

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Abstract

Kernel methods have been widely applied in machine learning to solve complex nonlinear problems. Kernel selection is one of the key issues in kernel methods, since it is vital for improving generalization performance. Traditionally, the selection of kernel is restricted to be positive definite which makes their applicability partially limited. Actually, in many real applications such as gene identification and object recognition, indefinite kernels frequently emerge and can achieve better performance. However, compared to positive definite ones, indefinite kernels are more complicated due to the non-convexity of the subsequent optimization problems, which leads to the incapability of most existing kernel algorithms. Some indefinite kernel methods have been proposed based on the dual of support vector machine (SVM), which mostly emphasize on how to transform the non-convex optimization to be convex by using positive definite kernels to approximate indefinite ones. In fact, the duality gap in SVM usually exists in the case of indefinite kernels and therefore these algorithms do not indeed solve the indefinite kernel problems themselves. In this paper, we present a novel framework for indefinite kernel learning derived directly from the primal of SVM, which establishes several new models not only for single indefinite kernel but also extends to multiple indefinite kernel scenarios. Several algorithms are developed to handle the non-convex optimization problems in these models. We further provide a constructive approach for kernel selection in the algorithms by using the theory of similarity functions. Experiments on real world datasets demonstrate the superiority of our models.

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Notes

  1. Here “stabilize” means finding a stationary point in a RKKS.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, Nanjing, 210096, People’s Republic of China

    Hui Xue & Lin Wang

  2. Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, People’s Republic of China

    Hui Xue & Lin Wang

  3. School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, People’s Republic of China

    Songcan Chen

  4. Department of Computer Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210016, People’s Republic of China

    Yunyun Wang

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  1. Hui Xue
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  2. Lin Wang
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  3. Songcan Chen
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  4. Yunyun Wang
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Correspondence to Hui Xue.

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This work was supported by the National Natural Science Foundations of China (Grant Nos. 61375057, 61300165 and 61403193), the Natural Science Foundation of Jiangsu Province of China (Grant No. BK20131298) and the National Key Research and Development Program of China (Grant Nos. 2016YFC1306700 and 2016YFC1306704). It was also supported by Collaborative Innovation Center of Wireless Communications Technology.

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Xue, H., Wang, L., Chen, S. et al. A Primal Framework for Indefinite Kernel Learning. Neural Process Lett 50, 165–188 (2019). https://doi.org/10.1007/s11063-019-10019-7

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