Title :
Non-Iterative Two-Dimensional Linear Discriminant Analysis
Author :
Inoue, Kohei ; Urahama, Kiichi
Author_Institution :
Dept. of Visual Commun. Design, Kyushu Univ., Fukuoka
Abstract :
Linear discriminant analysis (LDA) is a well-known scheme for feature extraction and dimensionality reduction of labeled data in a vector space. LDA has been extended to two-dimensional LDA (2DLDA), which is an iterative algorithm for data in matrix representation. In this paper, we propose non-iterative algorithms for 2DLDA. Experimental results show that the non-iterative algorithms achieve competitive recognition rates with the iterative 2DLDA, while they are computationally more efficient than the iterative 2DLDA
Keywords :
feature extraction; image recognition; feature extraction; iterative algorithm; labeled data dimensionality reduction; matrix representation; noniterative two-dimensional linear discriminant analysis; vector space; Feature extraction; Image converters; Iterative algorithms; Linear discriminant analysis; Matrix converters; Object recognition; Parallel algorithms; Scattering; Vectors; Visual communication;
Conference_Titel :
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2521-0
DOI :
10.1109/ICPR.2006.860
