Robust graph representation clustering based on adaptive data correction

被引:0
作者
Li Guo
Xiaoqian Zhang
Rui Zhang
Qian Wang
Xuqian Xue
Zhigui Liu
机构
[1] Southwest University of Science and Technology,School of Information Engineering
[2] Nanjing University of Science and Technology,School of Computer Science and Engineering
[3] Mianyang Weibo Electronic Co.,School of Optics and Electronics
[4] Ltd,undefined
[5] Beijing Institute of Technology,undefined
来源
Applied Intelligence | 2023年 / 53卷
关键词
Graph; Low rank; Clustering; Clean dictionary; Noise;
D O I
暂无
中图分类号
学科分类号
摘要
Impressive performance has been achieved when learning graphs from data in clustering tasks. However, real data often contain considerable noise, which leads to unreliable or inaccurate constructed graphs. In this paper, we propose adaptive data correction-based graph clustering (ADCGC), which can be used to adaptively remove errors and noise from raw data and improve the performance of clustering. The ADCGC method mainly contains three advantages. First, we design the weighted truncated Schatten p-norm (WTSpN) instead of the nuclear norm to recover the low-rank clean data. Second, we choose clean data samples that represent the essential properties of the data as the vertices of the undirected graph, rather than using all the data feature points. Third, we adopt the block-diagonal regularizer to define the edge weights of the graph, which helps to learn an ideal affinity matrix and improve the performance of clustering. In addition, an efficient iterative scheme based on the generalized soft-thresholding operator and alternating minimization is developed to directly solve the nonconvex optimization model. Experimental results show that ADCGC both quantitatively and visually outperforms existing advanced methods.
引用
收藏
页码:17074 / 17092
页数:18
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