.png){kind=icon}
返回
Nonlinear nonnegative matrix factorization based on Mercer kernel construction
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2011.03.023.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Generalizations ofnonnegative matrix factorization (NMF) in kernel feature space, such as projected gradient kernel NMF (PGKNMF) and polynomial Kernel NMF (PNMF), have been developed for face and facial expression recognition recently. However, these existing kernel NMF approaches cannot guarantee the nonnegativity of bases in kernel feature space and thus are essentially semi-NMF methods. In this paper, we show that nonlinear semi-NMF cannot extract the localized components which offer important information in object recognition. Therefore, nonlinear NMF rather than semi-NMF is needed to be developed for extracting localized component as well as learning the nonlinear structure. In order to address the nonlinear problem of NMF and the semi-nonnegative problem of the existing kernel NMF methods, we develop the nonlinear NMF based on a self-constructed Mercer kernel which preserves the nonnegative constraints on both bases and coefficients in kernel feature space. Experimental results in face and expressing recognition show that the proposed approach outperforms the existing state-of-the-art kernel methods, such as KPCA, GDA, PNMF and PGKNMF. (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Nonnegative matrix factorization
Mercer kernel
Kernel mapping
Face recognition
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏
分享收藏
Long-Term Health Monitoring of in-Service Bridge Deck by Covariance of Covariance Matrix of Acceleration Responses基于加速度响应协方差矩阵的在役桥面长期健康监测
PREAI
分享收藏分享收藏
查看更多内容