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. |
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| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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