EasyMKL: a scalable multiple kernel learning algorithm
EasyMKL: a scalable multiple kernel learning algorithm
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DOI：
10.1016/j.neucom.2014.11.078
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发表时间：
2015-12-02
期刊：
影响因子：
6
通讯作者：
Donini, Michele
中科院分区：
文献类型：
--
作者：
Aiolli, Fabio;Donini, Michele
多核学习(MKL)的目标是以数据驱动的方式组合来自多个来源的核，目的是提高目标核机器的精度。最先进的MKL方法的缺点是，解决相关优化问题所需的时间随着要组合的核的数量而增长(通常是线性以上)。此外，据经验观察，即使是复杂的方法，通常也不会显著优于简单的平均核函数。在本文中，我们提出了一种时间和空间高效的MKL算法，它可以轻松地处理数十万个或更多的核。提出的方法与其他基线(随机、平均等)进行了比较。三种最先进的MKL方法表明，我们的方法往往是优越的。实验表明，在加入噪声特征的情况下，本文提出的方法的优势更加明显。最后，我们分析了我们的算法如何随着训练集中的样本数量和组合的核数量的变化而改变其性能。(C)2015爱思唯尔B.V.保留所有权利。
The goal of Multiple Kernel Learning (MKL) is to combine kernels derived from multiple sources in a data-driven way with the aim to enhance the accuracy of a target kernel machine. State-of-the-art methods of MKL have the drawback that the time required to solve the associated optimization problem grows (typically more than linearly) with the number of kernels to combine. Moreover, it has been empirically observed that even sophisticated methods often do not significantly outperform the simple average of kernels. In this paper, we propose a time and space efficient MKL algorithm that can easily cope with hundreds of thousands of kernels and more. The proposed method has been compared with other baselines (random, average, etc.) and three state-of-the-art MKL methods showing that our approach is often superior. We show empirically that the advantage of using the method proposed in this paper is even clearer when noise features are added. Finally, we have analyzed how our algorithm changes its performance with respect to the number of examples in the training set and the number of kernels combined. (C) 2015 Elsevier B.V. All rights reserved.