Multivariate Pattern Classification based on Local Discriminant Component Analysis
Multivariate Pattern Classification based on Local Discriminant Component Analysis
复制标题
基于局部判别分量分析的多元模式分类
DOI：
10.1109/robio.2004.1521908
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
T. Tsuji
中科院分区：
文献类型：
--
作者：
N. Bu;T. Tsuji
本文提出了一种新的局部判别成分分析（DCA）算法，是有用的模式分类的高维数据。与大多数传统的方法中，通常使用的特征提取器之前的分类器，所提出的方法结合的特征提取过程中的分类器不同。然后，概率神经网络的思想的基础上开发的局部DCA，其中整个网络，包括特征提取器和分类器可以根据一个单一的训练标准进行调制，以便可以提取适合于分类目的的特征。本文在最小分类误差（MCE）学习的基础上，提出了一种混合训练算法。在仿真实验中，使用基准数据证明了该方法的可行性
This paper proposes a novel local discriminant component analysis (DCA) algorithm that is useful for pattern classification of high-dimensional data. Different from most traditional methods, in which feature extractors are usually used prior to a classifier, the proposed method incorporates the feature extraction process into the classifier. Then, a probabilistic neural network is developed based on the idea of local DCA, in which the whole network including the feature extractor and the classifier can be modulated according to a single training criterion, so that features suited to the classification purpose can be extracted. In this paper, a hybrid training algorithm is proposed on the basis of the minimum classification error (MCE) learning. In simulation experiments, benchmark data are used to prove feasibility of the proposed method