.png){kind=icon}
返回
Robust graph learning via constrained elastic-net regularization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2015.06.059.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Graph has been widely researched for characterizing data structure and successfully applied in many fields. To date, one popular kind of graph constructing methods is based on linear reconstruction coefficients. However, it is still a challenge to make the graph maintain the intra-class relations and diminish the inter-class relations. In this paper, we propose a robust graph learning method via a constrained elastic-net regularization (CEN). In CEN, the representation coefficients are imposed by a combination of Frobenius norm and weighted l(1)-norm. Among them, the weighted l(1)-norm benefits from our proposed shape interaction weighting (SIW) scheme to strengthen the intra-subspace compactness and enhance the inter-subspace separability. Moreover, the CEN model is extended with non-negative constraints for wild applications. We carry out experiments on real-world datasets to evaluate the effectiveness of the proposed framework. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Similarity learning
Graph construction
Local constraint
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:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
.png){kind=icon}
查看更多内容