Non-iterative two-dimensional linear discriminant analysis

被引：0

作者：

Inoue, Kohei ^{[1
]}

Urahama, Kiichi ^{[1
]}

机构：

[1] Kyushu Univ, Dept Visual Commun Design, Minami Ku, 4-9-1,Shiobaru, Fukuoka 8158540, Japan

来源：

18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS | 2006年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Linear discriminant analysis (LDA) is a well-known scheme for feature extraction and dimensionality reduction of labeled data in a vector space. Recently, 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.

引用

页码：540 / +

页数：2

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