.png){kind=icon}
返回
Robust non-negative matrix factorization with multiple correntropy-induced hypergraph regularizer
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2020.107569.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Non-negative matrix factorization (NMF) is a popular learning tool, which has widely used in computer vision and image processing. Many variants and extensions of NMF have been proposed, where the manifold regularized NMF methods have achieved promising performance due to the preservation of the geometric structures of the data. However, for many applications, the data is usually contaminated by complex noise. The noise leads the data to deviate from the intrinsic manifold, resulting in the degenerate performance. To make the NMF methods reflect the underlying manifold structure well, we propose a novel robust non-negative matrix factorization model. Our model proposes a novel ensemble manifold regularizer, which combines multiple robust hypergraphs to estimate the underlying manifold. Specifically, the correntropy-induced hypergraph is used as the initial manifold estimation. By incorporating the proposed regularizer into the original NMF framework, two novel manifold regularized NMF methods are proposed. The clustering results on the noisy image datasets demonstrate that our model is effective, which achieves the state-of-the-art performance. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Hypergraph
Robust
NMF
Noise
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:
3.6
论文数:
10.0K
被引数:
1.7W
机构
引用论文
分享收藏A sparse nonnegative matrix factorization technique for graph matching problems图匹配问题的稀疏非负矩阵分解技术.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Multi-focus image fusion based on non-negative matrix factorization and difference imagesSIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏Spatial Group Sparsity Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing用于高光谱分解的空间群稀疏正则化非负矩阵分解
PREAI
分享收藏
查看更多内容