点击次数：

论文类型：期刊论文

发表时间：2008-03-01

发表刊物：NEUROCOMPUTING

收录刊物：SCIE、EI

卷号：71

期号：7-9

页面范围：1735-1740

ISSN号：0925-2312

关键字：feature extraction; fuzzy membership degree; sample distribution; kernel methods; face recognition

摘要：Canonical correlation analysis (CCA) can extract more discriminative features by utilizing class labels, especially the ones that can reflect the sample distribution appropriately. In this paper, a new fuzzy approach for handling class labels in the form of fuzzy membership degrees is proposed. We elaborately design a novel fuzzy membership function to represent the distribution of image samples. These fuzzy class labels promote the classification performances of CCA and kernel CCA (KCCA) through incorporating distribution information into the process of feature extraction. Comprehensive experimental results on face recognition demonstrate the effectiveness and feasibility of the proposed method. (c) 2007 Elsevier B.V. All rights reserved.