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Sparse and Non-sparse Multiple Kernel Learning for Recognition This publication appears in: Computacion y Sistemas - An International journal of computing science and applications Authors: M. Perez Gonzalez, H. Sahli, I. Gonzalez and A. Taboada-Crispi Volume: 16 Issue: Machine Learning and Pattern Recognition Pages: 167-174 Publication Year: 2012
Abstract: The development of Multiple Kernel Techniques has become of particular interest for machine learning researchers in Computer Vision topics like image processing, object classification, and object state recognition. Sparsity-inducing norms along with non-sparse formulations promote different degrees of sparsity at the kernel coefficient level, at the same time permitting non-sparse combination within each individual kernel. This makes MKL models very suitable for different problems, allowing adequate selection of the regularizer according to different norms and the nature of the problem. We formulate and discuss MKL regularizations and optimization approaches, as well asdemonstrate MKL effectiveness compared to the stateof-the-art SVM models using a Computer Vision Recognition problem. External Link.
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