.png){kind=icon}
返回
Bipartite Graph Based Multi-View Clustering
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2020.3021649.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
For graph-based multi-view clustering, a critical issue is to capture consensus cluster structures via a two-stage learning scheme. Specifically, first learn similarity graph matrices of multiple views and then fuse them into a unified superior graph matrix. Most current methods learn pairwise similarities between data points for each view independently, which is widely used in single view. However, the consensus information contained in multiple views are ignored, and the involved biases lead to an undesirable unified graph matrix. To this end, we propose a bipartite graph based multi-view clustering (BIGMC) approach. The consensus information can be represented by a small number of representative uniform anchor points for different views. A bipartite graph is constructed between data points and the anchor points. BIGMC constructs the bipartite graph matrices of all views and fuses them to produce a unified bipartite graph matrix. The unified bipartite graph matrix in turn improves the bipartite graph similarity matrix of each view and updates the anchor points. The final unified graph matrix forms the final clusters directly. In BIGMC, an adaptive weight is added for each view to avoid outlier views. A low-rank constraint is imposed on the Laplacian matrix of the unified matrix to construct a multi-component unified bipartite graph, where the component number corresponds to the required cluster number. The objective function is optimized in an alternating optimization fashion. Experimental results on synthetic and real-world data sets demonstrate its effectiveness and superiority compared with the state-of-the-art baselines.
Keyword:
Bipartite graph
Clustering methods
Data mining
Kernel
Fuses
Laplace equations
Optimization
Multi-view clustering
similarity matrix
consensus information
bipartite graph
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:
10.4
论文数:
6.9K
被引数:
3.2W
机构
引用论文
Taurine modulation of hypochlorous acid-induced lung epithelial cell injury in vitro. Role of anion transport.牛磺酸对次氯酸诱导的体外肺上皮细胞损伤的调节作用。阴离子运输的作用。
OAAI
.png){kind=icon}
OAAI分享收藏Ecología urbana de Triatoma infestans en Argentina: asociación entre Triatoma infestans y palomares
OAAI
OAAI分享收藏Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification用于图像聚类和半监督分类的自动加权多视图学习
PREAI
分享收藏分享收藏分享收藏
查看更多内容