Abstract
In real applications, multi-view clustering with incomplete data has played an important role in the data mining field. How to design an algorithm to promote the clustering performance is a challenging problem. In this paper, we propose an approach with learned graph to handle the case that each view suffers from some missing information. It combines incomplete multi-view data and clusters it simultaneously by learning the ideal structures. For each view, with an initial input graph, it excavates a clustering structure with the consideration of consistency with the other views. The learned structured graphs have exactly c (the predefined number of clusters) connected components so that the clustering results can be obtained without requiring any post-clustering. An efficient optimization strategy is provided, which can simultaneously handle both the whole and the partial regularization problems. The proposed method exhibits impressive performance in experiments.
Supported by the National Natural Science Foundation of China (No. 61473302, 61503396).
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Wu, J., Zhuge, W., Tao, H., Hou, C., Zhang, Z. (2018). Incomplete Multi-view Clustering via Structured Graph Learning. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_8
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