Non-sparse Multiple Kernel Learning for Fisher Discriminant Analysis

—We consider the problem of learning a linear combination of pre-specified kernel matrices in the Fisher discriminant analysis setting. Existing methods for such a task impose an 1 norm regularisation on the kernel weights, which produces sparse solution but may lead to loss of information. In this paper, we propose to use 2 norm regularisation instead. The resulting learning problem is formulated as a semi-infinite program and can be solved efficiently. Through experiments on both synthetic data and a very challenging object recognition benchmark, the relative advantages of the proposed method and its 1 counterpart are demonstrated, and insights are gained as to how the choice of regularisation norm should be made. Keywords-Fisher Discriminant Analysis; Multiple Kernel Learning; Semi-Infinite Programming; Object Recognition

• Type: Conference
• Year: 2009
• Where: ICDM
• Authors: Fei Yan, Josef Kittler, Krystian Mikolajczyk, Muhammad Atif Tahir
• Keywords: Data Mining, Discriminant Analysis, Fisher Discriminant Analysis, ICDM 2009, Norm Regularisation