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Non-Sparse Multiple Kernel Fisher Discriminant Analysis

Yan, F, Kittler, J, Mikolajczyk, K and Tahir, A (2011) Non-Sparse Multiple Kernel Fisher Discriminant Analysis Journal of Machine Learning Research, 13. pp. 607-642.

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Sparsity-inducing multiple kernel Fisher discriminant analysis (MK-FDA) has been studied in the literature. Building on recent advances in non-sparse multiple kernel learning (MKL), we propose a non-sparse version of MK-FDA, which imposes a general `p norm regularisation on the kernel weights. We formulate the associated optimisation problem as a semi-infinite program (SIP), and adapt an iterative wrapper algorithm to solve it. We then discuss, in light of latest advances inMKL optimisation techniques, several reformulations and optimisation strategies that can potentially lead to significant improvements in the efficiency and scalability of MK-FDA. We carry out extensive experiments on six datasets from various application areas, and compare closely the performance of `p MK-FDA, fixed norm MK-FDA, and several variants of SVM-based MKL (MK-SVM). Our results demonstrate that `p MK-FDA improves upon sparse MK-FDA in many practical situations. The results also show that on image categorisation problems, `p MK-FDA tends to outperform its SVM counterpart. Finally, we also discuss the connection between (MK-)FDA and (MK-)SVM, under the unified framework of regularised kernel machines.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors : Yan, F, Kittler, J, Mikolajczyk, K and Tahir, A
Date : 19 July 2011
DOI : 10.1109/ICDM.2009.84
Contributors :
ContributionNameEmailORCID, S, F, C
Depositing User : Symplectic Elements
Date Deposited : 20 Nov 2012 11:34
Last Modified : 06 Jul 2019 05:11

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