.png){kind=icon}
返回
Low-rank matrix factorization with multiple Hypergraph regularizer
.png){kind=icon}
OA
AI
DOI：10.1016/j.patcog.2014.09.002.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a novel low-rank matrix factorization method, named MultiHMMF, which incorporates multiple Hypergraph manifold regularization to the low-rank matrix factorization. In order to effectively exploit high order information among the data samples, the Hypergraph is introduced to model the local structure of the intrinsic manifold. Specifically, multiple Hypergraph regularization terms are separately constructed to consider the local invariance; the optimal intrinsic manifold is constructed by linearly combining multiple Hypergraph manifolds. Then, the regularization term is incorporated into a truncated singular value decomposition framework resulting in a unified objective function so that matrix factorization is changed into an optimization problem. Alternating optimization is used to solve the optimization problem, with the result that the low dimensional representation of data space is obtained. The experimental results of image clustering demonstrate that the proposed method outperforms state-of-the-art data representation methods. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
Hypergraph
Matrix factorization
Manifold
Alternating optimization
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
Optimizing principal component analysis performance for face recognition using genetic algorithm.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏分享收藏Principal manifolds and nonlinear dimensionality reduction via tangent space alignment基于切线空间对齐的主流形和非线性降维
OAAI
OAAI分享收藏LF-EME: Local features with elastic manifold embedding for human action recognitionNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Tinnitus Retraining Therapy (TRT) as a Method for Treatment of Tinnitus and Hyperacusis Patients耳鸣再训练疗法 (TRT) 作为治疗耳鸣和高亢患者的方法
PREAI
分享收藏
查看更多内容