کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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406460 | 678086 | 2014 | 12 صفحه PDF | دانلود رایگان |
Kernel-based learning algorithms are well-known to poorly scale to large-scale applications. For such large tasks, a common solution is to use low-rank kernel approximation. Several algorithms and theoretical analyses have already been proposed in the literature, for low-rank Support Vector Machine or low-rank Kernel Ridge Regression but not for multiple kernel learning. The proposed method bridges this gap by addressing the problem of scaling ℓp-normℓp-norm multiple kernel for large learning tasks using low-rank kernel approximations. Our contributions stand on proposing a novel optimization problem, which takes advantage of the low-rank kernel approximations and on introducing a proximal gradient algorithm for solving that optimization problem. We also provide partial theoretical results on the impact of the low-rank approximations over the kernel combination weights. Experimental evidences show that the proposed approach scales better than the SMO-MKL algorithm for tasks involving about several hundred thousands of examples. Experimental comparisons with interior point methods also prove the efficiency of the algorithm we propose.
Journal: Neurocomputing - Volume 143, 2 November 2014, Pages 68–79