Multirate systems and filter banks
Multirate systems and filter banks
Local discriminant bases and their applications
Journal of Mathematical Imaging and Vision - Special issue on mathematical imaging
Hybrid wavelet-support vector classification of waveforms
Journal of Computational and Applied Mathematics
Feature extraction by shape-adapted local discriminant bases
Signal Processing
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A DC-programming algorithm for kernel selection
ICML '06 Proceedings of the 23rd international conference on Machine learning
Computers in Biology and Medicine
Atrial fibrillation classification with artificial neural networks
Pattern Recognition
Parametrizing compactly supported orthonormal wavelets by discrete moments
Applicable Algebra in Engineering, Communication and Computing
Proceedings of the 25th international conference on Machine learning
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Efficient wavelet adaptation for hybrid wavelet-large margin classifiers
Pattern Recognition
L2 regularization for learning kernels
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
On the space of orthonormal wavelets
IEEE Transactions on Signal Processing
Learning with infinitely many features
Machine Learning
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This paper addresses the problem of optimal feature extraction from a wavelet representation. Our work aims at building features by selecting wavelet coefficients resulting from signal or image decomposition on an adapted wavelet basis. For this purpose, we jointly learn in a kernelized large-margin context the wavelet shape as well as the appropriate scale and translation of the wavelets, hence the name ''wavelet kernel learning''. This problem is posed as a multiple kernel learning problem, where the number of kernels can be very large. For solving such a problem, we introduce a novel multiple kernel learning algorithm based on active constraints methods. We furthermore propose some variants of this algorithm that can produce approximate solutions more efficiently. Empirical analysis show that our active constraint MKL algorithm achieves state-of-the art efficiency. When used for wavelet kernel learning, our experimental results show that the approaches we propose are competitive with respect to the state-of-the-art on brain-computer interface and Brodatz texture datasets.