ADAPTIVE WEIGHTED MULTI-VIEW CLUSTERING BASED ON GRAPH
Aimed at the existing graph-based multi-view clustering algorithms without considering the weight of different views and their problem of noise in view data,a graph-based adaptive weighted multi-view clustering algorithm is proposed.Multiple relational graphs were constructed from the original data through adaptive neighborhood learning,and the view weight adjustment parameters were introduced to reduce the influence of noise.Each graph was integrated into a unified graph by adaptive learning,and the data points were automatically divided into clusters by rank constraint optimization,so as to obtain the clustering results.Experimental results on multi-view data sets show the effectiveness of the proposed algorithm.
国家科技期刊平台
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