PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multiple Kernel Learning and Feature Space Denoising
Fei Yan, Josef Kittler and Krystian Mikolajczyk
In: International Conference on Machine Learning and Cybernetics 2010, 11-14 Jul 2010, Qingdao, China.


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.

EPrint Type:Conference or Workshop Item (Invited Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
Multimodal Integration
ID Code:6991
Deposited By:Fei Yan
Deposited On:22 August 2010