CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network
This addresses the problem of clustering with missing views in multi-view data, which is incremental as it builds on existing shallow methods by introducing deep learning and noise reduction techniques.
The paper tackles incomplete multi-view clustering by proposing CDIMC-net, which uses deep encoders, graph embedding, and a self-paced strategy to improve representation learning and robustness, achieving state-of-the-art results on several datasets.
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, 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.