Summary of One-step Multi-view Clustering Based on Transition Probability, by Wenhui Zhao et al.
One-Step Multi-View Clustering Based on Transition Probability
by Wenhui Zhao, Quanxue Gao, Guangfei Li, Cheng Deng, Ming Yang
First submitted to arxiv on: 3 Mar 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 paper introduces a new multi-view clustering algorithm called One-Step Multi-View Clustering Based on Transition Probability (OSMVC-TP). This method uses a probabilistic approach to learn transition probabilities from anchor points to categories, enabling soft label matrices for samples and anchor points. The algorithm also incorporates a Schatten p-norm constraint to ensure consistency in labels across different views. The authors claim that OSMVC-TP effectively harnesses the complementary information among the views, making it more interpretable than existing methods. They demonstrate the effectiveness and robustness of their approach through extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to group similar things together (called clustering) when we have multiple ways to look at them. The method is called One-Step Multi-View Clustering Based on Transition Probability, or OSMVC-TP for short. It works by looking at how likely each thing is to be in a certain group based on the information from all the different views. This makes it easier to understand why things are being grouped together and helps keep the groups consistent across all the views. |
Keywords
* Artificial intelligence * Clustering * Probability
