An Efficient Approach to Integrating Radius Information into Multiple Kernel Learning
An Efficient Approach to Integrating Radius Information into Multiple Kernel Learning
复制标题
将半径信息集成到多核学习中的有效方法
DOI：
10.1109/tsmcb.2012.2212243
复制
发表时间：
2013-04-01
影响因子：
11.8
通讯作者：
Zhang, Jian
中科院分区：
文献类型：
--
作者：
Liu, Xinwang;Wang, Lei;Zhang, Jian
最近的多核学习（MKL）工作已经证明，集成半径信息是提高核学习性能的一种有前途的方法。然而，直接将最小包围球（MEB）的半径集成到MKL中，不仅会产生显着的计算开销，而且可能会对内核学习性能产生不利影响，因为该半径对异常值的敏感性是众所周知的。受MEB半径与总数据散射矩阵迹之间关系的启发，本文提出将后者纳入MKL中以改善这种情况。特别是，为了充分证明半径信息的纳入，我们严格遵守支持向量机（SVM）的半径边缘界，从而专注于l2范数软边缘SVM分类器。详细的理论分析表明，所提出的方法如何有效地保留了将MEB的半径的优点，以及如何有效地解决所产生的优化。此外，所提出的方法实现了以下优点：1）在存在离群值或噪声训练样本的情况下更鲁棒; 2）通过避免在每次迭代时计算半径的二次优化，计算效率更高;以及3）易于通过现有的现成MKL包求解。在加州大学Irvine分校、蛋白质亚细胞定位和Caltech-101数据集上进行了综合实验，实验结果很好地证明了该方法的有效性和高效性。
Integrating radius information has been demonstrated by recent work on multiple kernel learning (MKL) as a promising way to improve kernel learning performance. Directly integrating the radius of the minimum enclosing ball (MEB) into MKL as it is, however, not only incurs significant computational overhead but also possibly adversely affects the kernel learning performance due to the notorious sensitivity of this radius to outliers. Inspired by the relationship between the radius of the MEB and the trace of total data scattering matrix, this paper proposes to incorporate the latter into MKL to improve the situation. In particular, in order to well justify the incorporation of radius information, we strictly comply with the radius-margin bound of support vector machines (SVMs) and thus focus on the l2-norm soft-margin SVM classifier. Detailed theoretical analysis is conducted to show how the proposed approach effectively preserves the merits of incorporating the radius of the MEB and how the resulting optimization is efficiently solved. Moreover, the proposed approach achieves the following advantages over its counterparts: 1) more robust in the presence of outliers or noisy training samples; 2) more computationally efficient by avoiding the quadratic optimization for computing the radius at each iteration; and 3) readily solvable by the existing off-the-shelf MKL packages. Comprehensive experiments are conducted on University of California, Irvine, protein subcellular localization, and Caltech-101 data sets, and the results well demonstrate the effectiveness and efficiency of our approach.