Multi-view clustering based on graph-regularized nonnegative matrix factorization for object recognition

被引：77

作者：

Zhang, Xinyu ^{[1
]}

Gao, Hongbo ^{[2
]}

Li, Guopeng ^{[3
]}

Zhao, Jianhui ^{[4
]}

Huo, Jianghao ^{[4
]}

Yin, Jialun ^{[4
]}

Liu, Yuchao ^{[4
]}

Zheng, Li ^{[4
]}

机构：

[1] Tsinghua Univ, Informat Technol Ctr, Beijing 100084, Peoples R China

[2] Tsinghua Univ, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China

[3] Xian Commun Inst, Xian 710106, Shaanxi, Peoples R China

[4] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China

来源：

INFORMATION SCIENCES | 2018年 / 432卷

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

Multi-view; Clustering; Nonnegative matrix factorization (NMF); Graph regularization; Orthogonal constraint; SEGMENTATION; FUSION;

D O I：

10.1016/j.ins.2017.11.038

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Various datasets from sensors are used for object recognition, and different features may be extracted from the same dataset in processing. Different datasets thus describe representations or views of the same object. Fusing the information from this multi-view dataset can improve recognition performance. However, such different views have varying quality levels. In this paper, we discuss multi-view clustering based on graph-regularized nonnegative matrix factorization with fusing useful information effectively to improve recognition accuracy. Useful information is enhanced via graph embedding, and redundant information is removed using the orthogonal constraint in each view for clustering. Experimental results on several real datasets demonstrate the effectiveness of our approach in improving the clustering performance of datasets. (C) 2017 Elsevier Inc. All rights reserved.

引用

页码：463 / 478

页数：16

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