期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 32, 期 10, 页码 2014-2025出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2019.2913377
关键词
Clustering methods; Task analysis; Dimensionality reduction; Laplace equations; Visualization; Clustering algorithms; Biomedical optical imaging; Multi-view clustering; dimensionality reduction; structured graph learning
类别
资金
- National Natural Science Foundation of China [U1803263, 61772427, 61751202, 61871470]
In many real-world applications, we are often confronted with high dimensional data which are represented by various heterogeneous views. How to cluster this kind of data is still a challenging problem due to the curse of dimensionality and effectively integration of different views. To address this problem, we propose two parameter-free weighted multi-view projected clustering methods which perform structured graph learning and dimensionality reduction simultaneously. We can use the obtained structured graph directly to extract the clustering indicators, without performing other discretization procedures as previous graph-based clustering methods have to do. Moreover, two parameter-free strategies are adopted to learn an optimal weight for each view automatically, without introducing a regularization parameter as previous methods do. Extensive experiments on several public datasets demonstrate that the proposed methods outperform other state-of-the-art approaches and can be used more practically.
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