Summary of Multi-view Granular-ball Contrastive Clustering, by Peng Su et al.
Multi-view Granular-ball Contrastive Clustering
by Peng Su, Shudong Huang, Weihong Ma, Deng Xiong, Jiancheng Lv
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel Multi-view Granular-ball Contrastive Clustering (MGBCC) method combines instance-level and cluster-level multi-view contrastive learning approaches to tackle challenges in previous methods that either overlook local structures or introduce false negatives. MGBCC segments samples into coarse-grained granular balls, establishing associations between intra-view and cross-view balls, which are then reinforced in a shared latent space for multi-granularity contrastive learning. This approach preserves the local topological structure of the sample set, demonstrating improved model discriminability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MGBCC is a new way to learn from different views of data that keeps track of small groups (called granular balls) and how they relate to each other. This helps the method understand both big patterns (like clusters) and small details (like individual instances). MGBCC is better than previous methods because it doesn’t lose important information or make mistakes. |
Keywords
» Artificial intelligence » Clustering » Latent space
