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Kernel Learning Foundation

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Kernel Learning Algorithms for Face Recognition

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Abstract

Nonlinear information processing algorithms can be designed by means of linear techniques in implicit feature spaces induced by kernel functions. Kernel methods are algorithms that, by replacing the inner product with an appropriate positive definite function, implicitly perform a nonlinear mapping of the input data to a high-dimensional feature space.

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Authors and Affiliations

  1. Department of Automatic Test and Control, Harbin Institute of Technology, Yi-Kuang Street 2, Harbin, People’s Republic of China

    Jun-Bao Li

  2. School of Information and Engineering, Flinders University of South Australia, Sturt Road, Bedford Park, SA, 5042, Australia

    Shu-Chuan Chu

  3. HIT Shenzhen Graduate School, Harbin Institute of Technology, Room 424, Building D, Shenzhen Graduate School of Harbin Institute of Technology, Xili University Town, Nanshan,, Shenzhen City, Guangdong Province, People’s Republic of China

    Jeng-Shyang Pan

Authors
  1. Jun-Bao Li
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  2. Shu-Chuan Chu
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  3. Jeng-Shyang Pan
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Correspondence to Jun-Bao Li .

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Li, JB., Chu, SC., Pan, JS. (2014). Kernel Learning Foundation. In: Kernel Learning Algorithms for Face Recognition. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0161-2_3

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  • DOI: https://doi.org/10.1007/978-1-4614-0161-2_3

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-0160-5

  • Online ISBN: 978-1-4614-0161-2

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