Two-dimensional discriminant locality preserving projections (2DDLPP) and its application to feature extraction via fuzzy set

被引：0
作者：
Minghua Wan
Guowei Yang
Shan Gai
Zhangjing Yang
机构：
[1] Nanjing Audit University,School of Technology
[2] Nanjing University of Science and Technology,Key Laboratory of Intelligent Perception and Systems for High
[3] Nanjing Xiaozhuang University,Dimensional Information of Ministry of Education
来源：
Multimedia Tools and Applications | 2017年 / 76卷
关键词：
2DDLPP; Fuzzy set theory; Feature extraction; FKNN; Membership degree;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper presents a new method for image feature extraction, namely, the fuzzy 2D discriminant locality preserving projections (F2DDLPP) based on the 2D discriminant locality preserving projections (2DDLPP) and fuzzy set theory. Firstly, we calculate the membership degree matrix by fuzzy k-nearest neighbor (FKNN), then we incorporate the membership degree matrix into the definition of the intra-class scatter matrix and inter-class scatter matrix, respectively. Secondly, we can get the fuzzy intra-class scatter matrix and fuzzy inter-class scatter matrix, respectively. The FKNN is implemented to achieve the distribution information of original samples, and this information is utilized to redefine corresponding scatter matrices. So, F2DDLPP can extract discriminative features from overlapping (outlier) samples which is different to the conventional 2DDLPP. Finally, Experiments on the Yale, ORL face databases, USPS database and PolyU palmprint database are demonstrated to verify the effectiveness of the proposed algorithm.
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页码：355 / 371
页数：16
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