.png){kind=icon}
返回
lp-norm multiple kernel learning with low-rank kernels
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2014.06.019.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 l(p)-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. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Multiple kernel learning
Low-rank approximation
Kernels
SVM
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
ISOLATION OF AMYLOID P COMPONENT (PROTEIN AP) FROM NORMAL SERUM AS A CALCIUM-DEPENDENT BINDING PROTEIN.png)
The Lancet
The LancetIF0
PREAI
分享收藏SpicyMKL: a fast algorithm for Multiple Kernel Learning with thousands of kernelsMACHINE LEARNING
OAAI
MACHINE LEARNINGIF2.9
OAAI分享收藏分享收藏Solution structure of 5-keto-D-fructose: relevance to the specificity of hexose kinasesBiochemistry
BiochemistryIF0
PREAI
分享收藏
查看更多内容