Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17488
Title: Feature extraction using fuzzy inverse FDA
Authors: Yang, W
Wang, J
Ren, M
Zhang, L 
Yang, J
Keywords: Face recognition
FDA
Feature extraction
Fuzzy IFDA
Inverse FDA
Pulse signal recognition
Issue Date: 2009
Publisher: Elsevier
Source: Neurocomputing, 2009, v. 72, no. 13-15, p. 3384-3390 How to cite?
Journal: Neurocomputing 
Abstract: This paper proposes a new method of feature extraction and recognition, namely, the fuzzy inverse Fisher discriminant analysis (FIFDA) based on the inverse Fisher discriminant criterion and fuzzy set theory. In the proposed method, a membership degree matrix is calculated using FKNN, then the membership degree is incorporated into the definition of the between-class scatter matrix and within-class scatter matrix to get the fuzzy between-class scatter matrix and fuzzy within-class scatter matrix. Experimental results on the ORL, FERET face databases and pulse signal database show that the new method outperforms Fisherface, fuzzy Fisherface and inverse Fisher discriminant analysis.
URI: http://hdl.handle.net/10397/17488
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2009.03.011
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