Constrained NMF-Based Multi-View Clustering on Unmapped Data

Existing multi-view clustering algorithms require that the data is completely or partially mapped between each pair of views. However, this requirement could not be satisfied in most practical settings. In this paper, we tackle the problem of multi-view clustering for unmapped data in the framework of NMF based clustering. With the help of inter-view constraints, we define the disagreement between each pair of views by the fact that the indicator vectors of two instances from two different views should be similar if they belong to the same cluster and dissimilar otherwise. The overall objective of our algorithm is to minimize the loss function of NMF in each view as well as the disagreement between each pair of views. Experimental results show that, with a small number of constraints, the proposed algorithm gets good performance on unmapped data, and outperforms existing algorithms on partially mapped data and completely mapped data.

Authors

• Xianchao Zhang Dalian University of Technology
• Linlin Zong Dalian University of Technology
• Xinyue Liu Dalian University of Technology
• Hong Yu Dalian University of Technology

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