CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network

CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network

Jie Wen, Zheng Zhang, Yong Xu, Bob Zhang, Lunke Fei, Guo-Sen Xie

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 3230-3236. https://doi.org/10.24963/ijcai.2020/447

In recent years, incomplete multi-view clustering, which studies the challenging multi-view clustering problem on missing views, has received growing research interests. Although a series of methods have been proposed to address this issue, the following problems still exist: 1) Almost all of the existing methods are based on shallow models, which is difficult to obtain discriminative common representations. 2) These methods are generally sensitive to noise or outliers since the negative samples are treated equally as the important samples. In this paper, we propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), to address these issues. Specifically, it captures the high-level features and local structure of each view by incorporating the view-specific deep encoders and graph embedding strategy into a framework. Moreover, based on the human cognition, \emph{i.e.}, learning from easy to hard, it introduces a self-paced strategy to select the most confident samples for model training, which can reduce the negative influence of outliers. Experimental results on several incomplete datasets show that CDIMC-net outperforms the state-of-the-art incomplete multi-view clustering methods.
Keywords:
Machine Learning: Deep Learning
Machine Learning: Dimensionality Reduction and Manifold Learning
Data Mining: Clustering, Unsupervised Learning