Two-dimensional discriminant locality preserving projections (2DDLPP) and its application to feature extraction via fuzzy set |
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| Authors: | Minghua Wan Guowei Yang Shan Gai Zhangjing Yang |
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| Affiliation: | 1.School of Technology,Nanjing Audit University,Nanjing,China;2.Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education,Nanjing University of Science and Technology,Nanjing,China;3.Key Laboratory of Trusted Cloud Computing and Big Data Analysis,Nanjing Xiaozhuang University,Nanjing,People’s Republic of China |
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| Abstract: | 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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