Deep multi-view spectral clustering via ensemble

Mingyu Zhao, Weidong Yang, Feiping Nie

科研成果: 期刊稿件文章同行评审

19 引用 (Scopus)

摘要

Graph-based methods have achieved great success in multi-view clustering. However, existing graph-based models generally utilize shallow and linear embedding functions to obtain the common spectral embedding for clustering assignments. In addition, the fusion similarity graphs from multiple views are generally obtained by a simple weighted-sum rule. To this end, we propose a novel deep multi-view spectral clustering via ensemble model (DMCE), which applies ensemble clustering to fuse the similarity graphs from different views. On this basis, we employ the graph auto-encoder to learn the common spectral embedding, which can be regarded as the indicator matrix directly. Moreover, a unified optimization framework is designed to update the variables in the proposed DMCE, which consists of graph reconstruction loss, orthogonal loss, and graph contrastive learning loss. Extensive experiments on six real-world benchmark datasets have demonstrated the effectiveness of our model compared with the state-of-the-art multi-view clustering methods.

源语言英语
文章编号109836
期刊Pattern Recognition
144
DOI
出版状态已出版 - 12月 2023

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