Multiple Kernel Learning with Gaussianity Measure
Multiple Kernel Learning with Gaussianity Measure
复制标题
具有高斯性度量的多核学习
DOI：
10.1162/neco_a_00299
复制
发表时间：
2012
影响因子：
2.9
通讯作者：
Noboru Murata
中科院分区：
文献类型：
--
作者：
Hideitsu Hino;Nima Reyhani;Noboru Murata
核方法对于非线性多变量分析是有效的。核方法在实际应用中的一个主要问题是核的选择。关于核选择和核学习已经有了很多研究。多核学习（MKL）是一种很有前途的核优化方法。核方法适用于各种分类器，包括Fisher判别分析（FDA）。FDA给出了贝叶斯最优分类轴，如果在特征空间中的每个类的数据分布是一个共享的协方差结构的高斯。基于这一事实，提出了一个基于高斯性概念的MKL框架。作为具体实现，采用经验特征函数度量核函数凸组合特征空间的高斯性，并推导出两种MKL算法。从一些数据集上的实验结果，我们表明，提出的核学习，其次是FDA提供了强大的分类能力。
Kernel methods are known to be effective for nonlinear multivariate analysis. One of the main issues in the practical use of kernel methods is the selection of kernel. There have been a lot of studies on kernel selection and kernel learning. Multiple kernel learning (MKL) is one of the promising kernel optimization approaches. Kernel methods are applied to various classifiers including Fisher discriminant analysis (FDA). FDA gives the Bayes optimal classification axis if the data distribution of each class in the feature space is a gaussian with a shared covariance structure. Based on this fact, an MKL framework based on the notion of gaussianity is proposed. As a concrete implementation, an empirical characteristic function is adopted to measure gaussianity in the feature space associated with a convex combination of kernel functions, and two MKL algorithms are derived. From experimental results on some data sets, we show that the proposed kernel learning followed by FDA offers strong classification power.