Multiple Kernel Learning in Fisher Discriminant Analysis for Face Recognition
Multiple Kernel Learning in Fisher Discriminant Analysis for Face Recognition
复制标题
人脸识别费舍尔判别分析中的多核学习
DOI：
10.5772/52350
复制
发表时间：
2013-02-25
影响因子：
2.3
通讯作者：
Feng, Guo-Can
中科院分区：
文献类型：
--
作者：
Liu, Xiao-Zhang;Feng, Guo-Can
最近的应用和发展的基础上，支持向量机（SVM）已经表明，使用多个内核，而不是一个单一的可以提高分类器的性能。然而，很少有报告的性能基于核的Fisher判别分析（基于核的FDA）方法与多个核。提出了一种基于核的FDA多核构造方法。构造的核是几个基本核的线性组合，其权重受到约束。通过最大化的利润最大化准则（MMC），我们提出了一个迭代计划的重量优化。在FERET和CMU PIE人脸数据库上的实验表明，与单核Fisher判别分析相比，多核Fisher判别分析具有更高的识别性能。实验还表明，所构造的核在一定程度上放宽了基于核的FDA的参数选择。
Recent applications and developments based on support vector machines (SVMs) have shown that using multiple kernels instead of a single one can enhance classifier performance. However, there are few reports on performance of the kernel-based Fisher discriminant analysis (kernel-based FDA) method with multiple kernels. This paper proposes a multiple kernel construction method for kernel-based FDA. The constructed kernel is a linear combination of several base kernels with a constraint on their weights. By maximizing the margin maximization criterion (MMC), we present an iterative scheme for weight optimization. The experiments on the FERET and CMU PIE face databases show that, our multiple kernel Fisher discriminant analysis (MKFD) achieves high recognition performance, compared with single-kernel-based FDA. The experiments also show that the constructed kernel relaxes parameter selection for kernel-based FDA to some extent.