DocumentCode
457207
Title
Non-Iterative Two-Dimensional Linear Discriminant Analysis
Author
Inoue, Kohei ; Urahama, Kiichi
Author_Institution
Dept. of Visual Commun. Design, Kyushu Univ., Fukuoka
Volume
2
fYear
0
fDate
0-0 0
Firstpage
540
Lastpage
543
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location
Hong Kong
ISSN
1051-4651
Print_ISBN
0-7695-2521-0
Type
conf
DOI
10.1109/ICPR.2006.860
Filename
1699262
Link To Document