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Strategic Study of CAE >> 2007, Volume 9, Issue 2

A New Two_dimensional Linear Discriminant Analysis Algori thmBased on Fuzzy Set Theory

1. Department of Computer Science, Nanjing University of Science and Technology, Nanjing   210094, China ;

2. School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, China ;

3. Robotics Laboratory, Chinese Academy of Science, Shenyang   110015, China

Funding project:国家自然科学基金资助项目(60472060) Received: 2005-09-04 Revised: 2005-11-23

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Abstract

2DLDA algorithm is based on2D matrices and overleaps the step of transforming the matrices into the corresponding vectors,which is done on conventional LDA algorithm.However,performance of recognition rate may always be degraded by the overlapping(outlier)samples et al in the field of pattern recognition.How to avoid these shortcomings and extract optimal features to improve the performance of recognition is a key step. In this paper,a new2DLDA algorithm,named fuzzy2DLDA,is proposed.Fuzzy k-nearest neighbour(FKNN) is implemented first to achieve the distribution information of original samples represented with fuzzy membership degrees and is incorporated into the process of feature extraction.The proposed algorithm inherits the virtue of conventional2DLDA and suppresses the shortcoming resulted by overlappin g(outlier)samples et al. Experimental results on AT&T face database demonstrate rec ognition rates of the proposed algorithm outperform that of conventional2DLDA and fisherface.

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