Abstract In most of the existing multi-view subspace clustering methods, the consistent shared information of the multi-view data is learned, and the contribution of each view is regarded as equally important to integrate the difference information of multiple views. However, possible noise or redundancy between different views is ignored due to the idea of treating each view as equally important, resulting in poor final clustering performance. Therefore, an algorithm of adaptive weighted multi-view subspace clustering guided by manifold regularization(MR-AWMSC) is proposed in this paper. The consistent global low-rank representation information for each view is learned by nuclear norm, and difference information from different views is described by group effect. According to the concept of manifold regularization, the weight of each view is adaptively learned, and the contribution degree to the difference information of each view is automatically assigned. The difference information is integrated by the adaptive weight and the consistent information is fused to obtain the final consensus representation. The consensus representation is constructed for clustering multi-view data. Experimental results on six public datasets demonstrate that MR-AWMSC effectively improves the multi-view clustering performance.
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