Abstract:
The initial proximity matrix affects the performance of most proximity-based multi-view clustering methods. Initial value sensitivity problem. Tuning the initial proximity matrix is unrealistic for unsupervised clustering. Thus, proximity-based multi-view clustering’s initial value sensitivity problem is a major unsolved problem. This paper introduces multi-view consensus proximity learning (MCPL) to achieve this. The MCPL method learns the consensus proximity matrix to directly reflect the clustering result by self-weighting all views and constraining the Laplacian matrix. The proposed MCPL method learns the consensus proximity matrix using data representatives rather than the original data objects, unlike most multi-view proximity learning methods. To reduce the impact of the initial value on clustering performance, proximity learning will update data representatives. The method is tested extensively.
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