REPORT Orabona_Idiap-RR-07-2010/IDIAP Online-Batch Strongly Convex Multi Kernel Learning Orabona, Francesco Luo, Jie Caputo, Barbara EXTERNAL https://publications.idiap.ch/attachments/reports/2010/Orabona_Idiap-RR-07-2010.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/Orabona_CVPR_2010 Related documents Idiap-RR-07-2010 2010 Idiap April 2010 Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled ap- proach to combine multiple cues, and to obtain state-of-the- art performance. A general drawback of these strategies is the high computational cost during training, that prevents their application to large-scale problems. They also do not provide theoretical guarantees on their convergence rate. Here we present a Multiclass Multi Kernel Learning (MKL) algorithm that obtains state-of-the-art performance in a considerably lower training time. We generalize the standard MKL formulation to introduce a parameter that al- lows us to decide the level of sparsity of the solution. Thanks to this new setting, we can directly solve the problem in the primal formulation. We prove theoretically and experimen- tally that 1) our algorithm has a faster convergence rate as the number of kernels grow; 2) the training complexity is linear in the number of training examples; 3) very few iter- ations are enough to reach good solutions. Experiments on three standard benchmark databases support our claims. CONF Orabona_CVPR_2010/IDIAP Online-Batch Strongly Convex Multi Kernel Learning Orabona, Francesco Luo, Jie Caputo, Barbara EXTERNAL https://publications.idiap.ch/attachments/papers/2010/Orabona_CVPR_2010.pdf PUBLIC Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2010 June 2010 Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled ap- proach to combine multiple cues, and to obtain state-of-the- art performance. A general drawback of these strategies is the high computational cost during training, that prevents their application to large-scale problems. They also do not provide theoretical guarantees on their convergence rate. Here we present a Multiclass Multi Kernel Learning (MKL) algorithm that obtains state-of-the-art performance in a considerably lower training time. We generalize the standard MKL formulation to introduce a parameter that al- lows us to decide the level of sparsity of the solution. Thanks to this new setting, we can directly solve the problem in the primal formulation. We prove theoretically and experimen- tally that 1) our algorithm has a faster convergence rate as the number of kernels grow; 2) the training complexity is linear in the number of training examples; 3) very few iter- ations are enough to reach good solutions. Experiments on three standard benchmark databases support our claims.