Feature extraction using fuzzy complete linear discriminant analysis

Yan Cui, Zhong Jin
2012 2012 IEEE International Conference on Fuzzy Systems  
In pattern recognition, feature extraction techniques are widely employed to dimensionality reduction. In this paper, a novel feature extraction method, fuzzy complete linear discriminant analysis (Fuzzy-CLDA), is proposed by combining the complete linear discriminant analysis (CLDA) and the membership degrees of samples. Furthermore, we calculate the sample membership degrees with different distance metrics and compare the effectiveness of the distance metrics. In addition, experiments are provided for analyzing and illustrating our results.
doi:10.1109/fuzz-ieee.2012.6250813 dblp:conf/fuzzIEEE/CuiJ12 fatcat:5c64os2hq5dt5pnuljzeqfvycq