Summary of Interpretable Multi-view Clustering, by Mudi Jiang et al.
Interpretable Multi-View Clustering
by Mudi Jiang, Lianyu Hu, Zengyou He, Zhikui Chen
First submitted to arxiv on: 4 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 addresses the long-standing issue of developing interpretable methods for multi-view clustering, which is crucial for real-world applications where understanding why samples are assigned to particular clusters is essential. To achieve this, the authors introduce an interpretable multi-view clustering framework that combines decision tree optimization with pseudo-label generation and feature representation refinement. The method begins by extracting embedded features from each view and generates pseudo-labels to guide the initial construction of the decision tree. Subsequently, it iteratively optimizes the feature representation for each view along with refining the interpretable decision tree. Experimental results on real datasets demonstrate that this approach not only provides a transparent clustering process but also delivers performance comparable to state-of-the-art multi-view clustering methods. This paper fills a notable gap in developing interpretable methods for clustering multi-view data, opening up new avenues in this field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to group things into categories based on different characteristics. That’s what clustering is all about! But often, we need to understand why certain items are grouped together. This paper tries to fill a gap by creating a way to explain how clusters are formed when we have multiple types of data (like images and text). They use special techniques to make the process clear and transparent. The results show that this method not only helps us see why things are grouped, but also works well compared to other methods. |
Keywords
» Artificial intelligence » Clustering » Decision tree » Optimization
