logo Idiap Research Institute        
 [BibTeX] [Marc21]
Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning
Type of publication: Conference paper
Citation: Orabona_ICML_2011
Publication status: Accepted
Booktitle: Proceedings of the 28th International Conference on Machine Learning
Year: 2011
Month: June
Crossref: Orabona_Idiap-RR-11-2011:
Abstract: Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise between performance, sparsity of the solution and speed of the optimization process. In this paper we look at the MKL problem at the same time from a learning and optimization point of view. So, instead of designing a regularizer and then struggling to find an efficient method to minimize it, we design the regularizer while keeping the optimization algorithm in mind. Hence, we introduce a novel MKL formulation, which mixes elements of p-norm and elastic-net kind of regularization. We also propose a fast stochastic gradient descent method that solves the novel MKL formulation. We show theoretically and empirically that our method has 1) state-of-the-art performance on many classification tasks; 2) ex- act sparse solutions with a tunable level of sparsity; 3) a convergence rate bound that depends only logarithmically on the num- ber of kernels used, and is independent of the sparsity required; 4) independence on the particular convex loss function used.
Projects Idiap
Authors Orabona, Francesco
Luo, Jie
Added by: [UNK]
Total mark: 0
  • Orabona_ICML_2011.pdf