Zafeiriou Stefanos, Tefas Anastasios, Buciu Ioan, Pitas Ioannis
Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54006, Greece.
IEEE Trans Neural Netw. 2006 May;17(3):683-95. doi: 10.1109/TNN.2006.873291.
In this paper, two supervised methods for enhancing the classification accuracy of the Nonnegative Matrix Factorization (NMF) algorithm are presented. The idea is to extend the NMF algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. The first method employs discriminant analysis in the features derived from NMF. In this way, a two-phase discriminant feature extraction procedure is implemented, namely NMF plus Linear Discriminant Analysis (LDA). The second method incorporates the discriminant constraints inside the NMF decomposition. Thus, a decomposition of a face to its discriminant parts is obtained and new update rules for both the weights and the basis images are derived. The introduced methods have been applied to the problem of frontal face verification using the well-known XM2VTS database. Both methods greatly enhance the performance of NMF for frontal face verification.
本文提出了两种用于提高非负矩阵分解（NMF）算法分类准确率的监督方法。其思路是扩展NMF算法，以便提取不仅能强化空间局部性，还能以判别方式增强类间可分性的特征。第一种方法在NMF导出的特征中采用判别分析。通过这种方式，实现了一个两阶段的判别特征提取过程，即NMF加线性判别分析（LDA）。第二种方法将判别约束纳入NMF分解中。这样，就得到了将人脸分解为其判别部分的结果，并推导出了权重和基图像的新更新规则。所提出的方法已应用于使用著名的XM2VTS数据库进行的正面人脸验证问题。两种方法都极大地提高了NMF用于正面人脸验证的性能。
