Letters: Two-stage dimensionality reduction approach based on 2DLDA and fuzzy rough sets technique

Authors:
Hao-Xin Zhao;Hong-Jie Xing;Xi-Zhao Wang
Affiliations:
Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science, Hebei University, Baoding 071002, Hebei Province, China;Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science, Hebei University, Baoding 071002, Hebei Province, China;Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science, Hebei University, Baoding 071002, Hebei Province, China
Venue:
Neurocomputing
Year:
2011

Citing 9
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Abstract

Traditional two-dimensional linear discriminant analysis (2DLDA) can deal with discriminant information between classes and directly extract features from image matrices. However, 2DLDA essentially works solely in the row-direction of images. Therefore, the features extracted by 2DLDA may contain redundant information. In this letter, a dimensionality reduction method based on 2DLDA and fuzzy rough sets technique is proposed to deal with the foresaid problem. Experimental results on the four benchmark face databases demonstrate that the proposed method is superior to its related methods.