聚类分析
光谱聚类
计算机科学
嵌入
符号
相关聚类
图形
数据挖掘
理论计算机科学
算法
人工智能
数学
算术
作者
Chang Tang,Zhenglai Li,Jun Wang,Xinwang Liu,Wei Zhang,En Zhu
出处
期刊:IEEE Transactions on Knowledge and Data Engineering
[Institute of Electrical and Electronics Engineers]
日期:2023-06-01
卷期号:35 (6): 6449-6460
被引量:55
标识
DOI:10.1109/tkde.2022.3172687
摘要
Multi-view spectral clustering, which exploits the complementary information among graphs of diverse views to obtain superior clustering results, has attracted intensive attention recently. However, most existing multi-view spectral clustering methods obtain the clustering partitions in a two-step scheme, i.e., spectral embedding and subsequent $k$ -means. This two-step scheme inevitably seeks sub-optimal clustering results due to the information loss during the two-steps processes. Besides, existing multi-view spectral clustering methods do not jointly utilize the information of graphs and embedding matrices, which also degrades final clustering results. To solve these issues, we propose a unified one-step multi-view spectral clustering method, which integrates the spectral embedding and $k$ -means into a unified framework to obtain discrete clustering labels with a one-step strategy. Under the observation that the inner product of the embedding matrix is a low-rank approximation of the graph, we combine graphs and embedding matrices of different views to obtain a unified graph. Then, we directly capture the discrete clustering indicator matrix from the unified graph. Furthermore, we design an effective optimization algorithm to solve the resultant problem. Finally, a set of experiments on various datasets are conducted to verify the effectiveness of the proposed method. The demo code of this work is publicly available at r gb]0,0,1 https://github.com/guanyuezhen/UOMvSC .
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