Semi-Supervised Subspace Clustering Based on Tensor Low-Rank Representation with Weighted Tensor Schatten-p Norm
Qi Ping Cao, Mu Rong Yang, Jianping Hu, Jinni Yu · 2024
In this paper, semi-supervised subspace clustering based on tensor low-rank representation is proposed to do the clustering task. In our paper, we propose to use the weighted tensor Schatten-p norm to approximate tensor multi-rank. Besides,we adjust the data structure of the tensor model to reduce the computational time of t-SVD in our method to improve the computational efficiency. Eventually, experimental results demonstrate that our method outperforms other methods and the running time is much shorter than those methods.