.png){kind=icon}
返回
Structured general and specific multi-view subspace clustering
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2019.05.005.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we propose a structured general and specific multi-view subspace clustering method for image clustering. Unlike most existing multi-view subspace clustering methods which harness the shared cluster structure to preserve the consistence between different views or utilize the diversity regularization to exploit the complementary information from different views, our method learns the structured general and specific representation matrices to obtain the common and specific characteristics of different views with structure consistence and diversity regularization. The general representation matrix guarantees the consistence between different views and the specific representation matrices indicate the diversity among different views. Hence, our method can well exploit the common structure and diversity information of multi-view data. Specifically, the proposed framework can be applied into many existing multi-view subspace clustering methods. Moreover, we develop an efficient and effective optimization approach to solve the objective function of which the time and convergence analyses are also provided. Experimental results on four benchmark datasets are presented to show the effectiveness of proposed method. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Subspace clustering
Multi-view learning
Structure consistence
Diversity
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
分享收藏Soft subspace clustering of categorical data with probabilistic distance.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Distance metric learning for soft subspace clustering in composite kernel spacePATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏Evaluating the Interpersonal Needs Questionnaire: Comparison of the Reliability, Factor Structure, and Predictive Validity across Five Versions评估人际需求问卷: 五个版本的可靠性，因素结构和预测效度的比较
PREAI
分享收藏
查看更多内容