Summary of Consistency Enhancement-based Deep Multiview Clustering Via Contrastive Learning, by Hao Yang et al.
Consistency Enhancement-Based Deep Multiview Clustering via Contrastive Learning
by Hao Yang, Hua Mao, Wai Lok Woo, Jie Chen, Xi Peng
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 a novel deep multiview clustering (MVC) method, called Consistent Enhancement-based Deep MVC via Contrastive Learning (CCEC). The approach leverages contrastive learning to preserve consistent information across multiple views and enhance feature representations for clustering. By incorporating semantic connection blocks into the feature representation process, CCEC improves clustering consistency while outperforming state-of-the-art methods on five benchmark datasets. This method has potential applications in various fields where data is represented from different perspectives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to group similar objects together based on how they look from different angles. This can be tricky because the same object might look very different depending on the angle and lighting. Researchers have developed a new way to do this, called deep multiview clustering, which helps machines understand patterns in data that comes from multiple views or perspectives. The goal is to create groups of similar objects that make sense across all angles. This paper shows how to improve this process by combining different techniques to get the best results. |
Keywords
* Artificial intelligence * Clustering
