关键词：
multi-view clustering
tensor log-determinant function
subspace learning
hypergraph regularization
摘要：
The existing multi-view subspace clustering algorithms based on tensor singular value decomposition(t-SVD)predominantly utilize tensor nuclear norm to explore the intra view correlation between views of the same samples,while neglecting the correlation among the samples within different ***,the tensor nuclear norm is not fully considered as a convex approximation of the tensor rank *** different singular values equally may result in suboptimal tensor representation.A hypergraph regularized multi-view subspace clustering algorithm with dual tensor log-determinant(HRMSC-DTL)was *** algorithm used subspace learning in each view to learn a specific set of affinity matrices,and introduced a non-convex tensor log-determinant function to replace the tensor nuclear norm to better improve global *** also introduced hyper-Laplacian regularization to preserve the local geometric structure embedded in the high-dimensional ***,it rotated the original tensor and incorporated a dual tensor mechanism to fully exploit the intra view correlation of the original tensor and the inter view correlation of the rotated *** the same time,an alternating direction of multipliers method(ADMM)was also designed to solve non-convex optimization *** evaluations on seven widely used datasets,along with comparisons to several state-of-the-art algorithms,demonstrated the superiority and effectiveness of the HRMSC-DTL algorithm in terms of clustering performance.
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中文科技期刊
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