Comparing Linear Discriminant Analysis and Support Vector Machines
ADVIS '02 Proceedings of the Second International Conference on Advances in Information Systems
On different facets of regularization theory
Neural Computation
Kernel-based nonlinear blind source separation
Neural Computation
Kernel VA-files for relevance feedback retrieva
MMDB '03 Proceedings of the 1st ACM international workshop on Multimedia databases
Stochastic orthogonal and nonorthogonal subspace basis pursuit
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Intra-personal kernel space for face recogni
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Tree-dependent component analysis
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Blind signal separation on real data: tracking and implementation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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We present a class of algorithms for Independent Component Analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On the one hand, we show that our contrast functions are related to mutual information and have desirable mathematical properties as measures of statistical dependence. On the other hand, building on recent developments in kernel methods, we show that these criteria and their derivatives can be computed efficiently. Minimizing these criteria leads to flexible and robust algorithms for ICA. We illustrate with simulations involving a wide variety of source distributions, showing that our algorithms outperform many of the presently known algorithms.