Summary of Cdimc-net: Cognitive Deep Incomplete Multi-view Clustering Network, by Jie Wen et al.
CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network
by Jie Wen, Zheng Zhang, Yong Xu, Bob Zhang, Lunke Fei, Guo-Sen Xie
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), a novel method for incomplete multi-view clustering, which aims to address the limitations of existing shallow models. The proposed approach incorporates view-specific deep encoders and graph embedding strategy to capture high-level features and local structure of each view. Additionally, CDIMC-net introduces a self-paced strategy to select the most confident samples for model training, reducing the negative influence of outliers. Experimental results show that CDIMC-net outperforms state-of-the-art incomplete multi-view clustering methods on several datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about fixing a problem in machine learning called incomplete multi-view clustering. It’s like trying to group people together based on different characteristics, but some of the information is missing. Most previous solutions were simple and didn’t work well when there was noise or mistakes. The new method, CDIMC-net, uses deep learning and special algorithms to find the best groups despite the missing information. It also learns from easy cases first and then moves on to harder ones, which helps it ignore bad data. The results show that this approach works better than others in similar situations. |
Keywords
* Artificial intelligence * Clustering * Deep learning * Embedding * Machine learning
