文献类型：期刊文献

英文题名：Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix

作者：Shi, Zonghan[1];Zhao, Haitao[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Automat Dept, Shanghai 200237, Peoples R China

年份：2023

卷号：13

期号：15

外文期刊名：APPLIED SCIENCES-BASEL

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001045446400001)】；

基金：This research was funded by the National Natural Science Foundation of China (NSFC) under Grant 62173143 and Grant 61973122.

语种：英文

外文关键词：multi-view learning; subspace clustering; self-expressive matrix; deep learning

摘要：Deep Multi-view Subspace Clustering is a powerful unsupervised learning technique for clustering multi-view data, which has achieved significant attention during recent decades. However, most current multi-view clustering methods rely on learning self-expressive layers to obtain the ultimate clustering results, where the size of the self-expressive matrix increases quadratically with the number of input data points, making it difficult to handle large-scale datasets. Moreover, since multiple views are rich in information, both consistency and specificity of the input images need to be considered. To solve these problems, we propose a novel deep multi-view clustering approach based on the reconstructed self-expressive matrix (DCRSM). We use a reconstruction module to approximate self-expressive coefficients using only a small number of training samples, while the conventional self-expressive model must train the network with entire datasets. We also use shared layers and specific layers to integrate consistent and specific information of features to fuse information between views. The proposed DCRSM is extensively evaluated on multiple datasets, including Fashion-MNIST, COIL-20, COIL-100, and YTF. The experimental results demonstrate its superiority over several existing multi-view clustering methods, achieving an improvement between 1.94% and 4.2% in accuracy and a maximum improvement of 4.5% in NMI across different datasets. Our DCRSM also yields competitive results even when trained by 50% samples of the whole datasets.