Gradient descent fisher non-negative matrix factorization for face recognition

被引：0

作者：

Liu, Yuhao ^{[1
]}

Jia, Chengcheng ^{[1
]}

Li, Bin ^{[1
]}

Yu, Zhezhou ^{[1
]}

机构：

[1] College of Computer Science and Technology, Jilin University

来源：

Journal of Information and Computational Science | 2013年 / 10卷 / 08期

关键词：

Dimensionality reduction; Face recognition; Fisher constraint; Gradient descent method; Nonnegative Matrix Factorization (NMF);

D O I：

10.12733/jics20101775

中图分类号：

学科分类号：

摘要：

In this paper, we propose a novel subspace method called Gradient descent Fisher Non-Negative Matrix Factorization (GdFNMF) for face recognition. By imposing fisher constraints and taking use of Euclidean distance as the measure of the cost function into gradient descent method, our GdFNMF can encode discrimination information for the classification problem. Experiments show that our GdFNMF achieves better performance than FNMF and less sensitive to the value of parameter. Copyright © 2013 Binary Information Press.

引用

页码：2453 / 2461

页数：8

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