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Multiple Kernel Learning and Feature Space Denoising

Yan, F, Kittler, J and Mikolajczyk, K (2010) Multiple Kernel Learning and Feature Space Denoising In: 2010 International Conference on Machine Learning and Cybernetics (ICMLC), 2010-07-11 - 2010-07-14, Quingdao.

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We review a multiple kernel learning (MKL) technique called ℓp regularised multiple kernel Fisher discriminant analysis (MK-FDA), and investigate the effect of feature space denoising on MKL. Experiments show that with both the original kernels or denoised kernels, ℓp MK-FDA outperforms its fixed-norm counterparts. Experiments also show that feature space denoising boosts the performance of both single kernel FDA and ℓp MK-FDA, and that there is a positive correlation between the learnt kernel weights and the amount of variance kept by feature space denoising. Based on these observations, we argue that in the case where the base feature spaces are noisy, linear combination of kernels cannot be optimal. An MKL objective function which can take care of feature space denoising automatically, and which can learn a truly optimal (non-linear) combination of the base kernels, is yet to be found.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Yan, F
Kittler, J
Mikolajczyk, K
Date : 20 September 2010
DOI : 10.1109/ICMLC.2010.5580970
Contributors :
Additional Information : © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 21 Dec 2012 12:18
Last Modified : 31 Oct 2017 14:49

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