School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Comput Math Methods Med. 2013;2013:275317. doi: 10.1155/2013/275317. Epub 2013 Oct 7.
In pattern recognition, feature extraction techniques have been widely employed to reduce the dimensionality of high-dimensional data. In this paper, we propose a novel feature extraction algorithm called membership-degree preserving discriminant analysis (MPDA) based on the fisher criterion and fuzzy set theory for face recognition. In the proposed algorithm, the membership degree of each sample to particular classes is firstly calculated by the fuzzy k-nearest neighbor (FKNN) algorithm to characterize the similarity between each sample and class centers, and then the membership degree is incorporated into the definition of the between-class scatter and the within-class scatter. The feature extraction criterion via maximizing the ratio of the between-class scatter to the within-class scatter is applied. Experimental results on the ORL, Yale, and FERET face databases demonstrate the effectiveness of the proposed algorithm.
在模式识别中，特征提取技术被广泛应用于降低高维数据的维度。本文提出了一种新的特征提取算法，称为基于 Fisher 准则和模糊集理论的隶属度保持判别分析（MPDA），用于人脸识别。在该算法中，首先通过模糊 k-最近邻（FKNN）算法计算每个样本对特定类别的隶属度，以刻画每个样本与类中心之间的相似性，然后将隶属度纳入类间散度和类内散度的定义中。通过最大化类间散度与类内散度的比值来进行特征提取准则。在 ORL、Yale 和 FERET 人脸数据库上的实验结果表明了该算法的有效性。
