Non-iterative Symmetric Two-Dimensional Linear Discriminant Analysis

被引：3

作者：

Inoue, Kohei ^{[1
]}

Hara, Kenji ^{[1
]}

Urahama, Kiichi ^{[1
]}

机构：

[1] Kyushu Univ, Fac Design, Fukuoka 8158540, Japan

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2011年 / E94D卷 / 04期

关键词：

symmetric two-dimensional linear discriminant analysis; face recognition; dimensionality reduction;

D O I：

10.1587/transinf.E94.D.926

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Linear discriminant analysis (LDA) is one of the well-known schemes for feature extraction and dimensionality reduction of labeled data. Recently, two-dimensional LDA (2DLDA) for matrices such as images has been reformulated into symmetric 2DLDA (S2DLDA), which is solved by an iterative algorithm. In this paper, we propose a non-iterative S2DLDA and experimentally show that the proposed method achieves comparable classification accuracy with the conventional S2DLDA, while the proposed method is computationally more efficient than the conventional S2DLDA.

引用

页码：926 / 929

页数：4

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