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Feature extraction using fuzzy complete linear discriminant analysis
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