Subclass discriminant analysis.
Subclass discriminant analysis.
复制标题
子类判别分析。
DOI：
10.1109/tpami.2006.172
复制
发表时间：
2006
期刊：
影响因子：
--
通讯作者：
Martinez,AleixM
中科院分区：
文献类型：
--
作者：
Zhu,Manli;Martinez,AleixM
多年来，人们提出了许多判别分析(DA)算法来研究高维数据中的各种问题。这些算法中的每一种都针对特定类型的数据分布(对手头的问题进行最佳建模的数据分布)进行调整。不幸的是，在大多数问题中，每一类pdf的形式都是先验未知的，而最适合我们数据的DA算法的选择是通过反复试验完成的。理想情况下，人们希望有一种可用于大多数分发类型的单一配方。这可以通过用混合的高斯近似每一类的基本分布来实现。在这种方法中，要解决的主要问题是确定每类的最佳高斯数目，即子类的数目。本文给出了两个最方便地将每一类划分为一组子类的判据。使用五个数据库给出了广泛的实验结果。与线性判别分析(LDA)、直接判别分析(DLDA)、异方差判别分析(HLDA)、非参数判别分析(NDA)和基于核的判别分析(K-LDA)进行了比较。我们证明，我们的方法总是最好的，或者可以与最好的方法相媲美。
Over the years, many discriminant analysis (DA) algorithms have been proposed for the study of high-dimensional data in a large variety of problems. Each of these algorithms is tuned to a specific type of data distribution (that which best models the problem at hand). Unfortunately, in most problems the form of each class pdf is a priori unknown, and the selection of the DA algorithm that best fits our data is done over trial-and-error. Ideally, one would like to have a single formulation which can be used for most distribution types. This can be achieved by approximating the underlying distribution of each class with a mixture of Gaussians. In this approach, the major problem to be addressed is that of determining the optimal number of Gaussians per class, i.e., the number of subclasses. In this paper, two criteria able to find the most convenient division of each class into a set of subclasses are derived. Extensive experimental results are shown using five databases. Comparisons are given against linear discriminant analysis (LDA), direct LDA (DLDA), heteroscedastic LDA (HLDA), nonparametric DA (NDA), and kernel-based LDA (K-LDA). We show that our method is always the best or comparable to the best.