.png){kind=icon}
返回
Parameter-less Auto-weighted multiple graph regularized Nonnegative Matrix Factorization for data representation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2017.05.029.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recently, multiple graph regularizer based methods have shown promising performances in data representation. However, the parameter choice of the regularizer is crucial to the performance of clustering and its optimal value changes for different real datasets. To deal with this problem, we propose a novel method called Parameter-less Auto-weighted Multiple Graph regularized Nonnegative Matrix Factorization (PAMGNMF) in this paper. PAMGNMF employs the linear combination of multiple simple graphs to approximate the manifold structure of data as previous methods do. Moreover, the proposed method can automatically learn an optimal weight for each graph without introducing an additive parameter. Therefore, the proposed PAMGNMF method is easily applied to practical problems. Extensive experimental results on different real-world datasets have demonstrated that the proposed method achieves better performance than the state-of-the-art approaches. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Data representation
Multiple graph
NMF
Parameter-less
Manifold
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
K
IF:
7.6
论文数:
1.2W
被引数:
4.5W
机构
引用论文
分享收藏A Review of Technical Impact of Electrical Vehicle Charging Stations on Distribution Grid电动汽车充电站对配电网的技术影响研究综述
PREAI
分享收藏Multiple graph regularized sparse coding and multiple hypergraph regularized sparse coding for image representation用于图像表示的多图正则化稀疏编码和多超图正则化稀疏编码.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容