Auto-weighted Tensor Schatten p-Norm for Robust Multi-view Graph Clustering

作者：

Highlights：

• A novel t-ATSN norm is proposed to better approximate the target rank of the learned graph tensor and make full use of prior information of singular values.

• An enhanced adaptive neighbors graph learning is proposed to greatly alleviate the influence of noise, guiding to learn the more clean affinity graph.

• Experimental results of our method are consistently superior to the literatures.

摘要

•A novel t-ATSN norm is proposed to better approximate the target rank of the learned graph tensor and make full use of prior information of singular values.•An enhanced adaptive neighbors graph learning is proposed to greatly alleviate the influence of noise, guiding to learn the more clean affinity graph.•Experimental results of our method are consistently superior to the literatures.

论文关键词：Multi-view clustering,Adaptive neighbors graph learning,Low-rank tensor learning,Noise estimation

论文评审过程：Received 9 February 2022, Revised 6 August 2022, Accepted 27 September 2022, Available online 30 September 2022, Version of Record 7 October 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.109083