The theoretical analysis of FDA and applications
The theoretical analysis of FDA and applications
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DOI：
10.1016/j.patcog.2005.09.018
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发表时间：
2006-06
期刊：
影响因子：
--
通讯作者：
Qing Tao;Gao-wei Wu;Jue Wang
中科院分区：
文献类型：
--
作者：
Qing Tao;Gao-wei Wu;Jue Wang
表示和嵌入通常是设计分类器的两个必要阶段。Fisher判别分析（FDA）被认为是寻找一个方向，投影样本是很好的分离。在本文中，我们分析FDA的表示和嵌入。本文的主要贡献是证明了FDA的一般框架是基于最简单、最直观的类内方差为零的FDA，从而清楚地说明了FDA的机制。基于我们的分析，ε-不敏感的SVM回归可以被看作是一个软FDA与ε-不敏感的类内方差和L1范数惩罚。为了验证这一观点，进行了几个真实的分类实验，以证明基于回归的分类技术的性能与常规FDA和SVM相当。
Representation and embedding are usually the two necessary phases in designing a classifier. Fisher discriminant analysis (FDA) is regarded as seeking a direction for which the projected samples are well separated. In this paper, we analyze FDA in terms of representation and embedding. The main contribution is that we prove that the general framework of FDA is based on the simplest and most intuitive FDA with zero within-class variance and therefore the mechanism of FDA is clearly illustrated. Based on our analysis, ε-insensitive SVM regression can be viewed as a soft FDA with ε-insensitive within-class variance and L1norm penalty. To verify this viewpoint, several real classification experiments are conducted to demonstrate that the performance of the regression-based classification technique is comparable to regular FDA and SVM.