Summary of Scalable Multi-view Clustering Via Explicit Kernel Features Maps, by Chakib Fettal et al.
Scalable Multi-view Clustering via Explicit Kernel Features Maps
by Chakib Fettal, Lazhar Labiod, Mohamed Nadif
First submitted to arxiv on: 7 Feb 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 introduces a new scalability framework for multi-view subspace clustering, which is essential for large-scale datasets. The framework utilizes kernel feature maps to reduce computational burden while maintaining good clustering performance. This allows the algorithm to be applied to datasets with millions of data points using standard machines within minutes. The authors evaluate their approach against state-of-the-art methods and attributed-network approaches on various benchmark networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to group similar things together in big datasets. Imagine you have a huge list of people, each with many characteristics like age, location, and interests. The algorithm helps find groups of people who share similar traits, but it’s fast even for massive lists. This is important because we often need to analyze data from multiple sources, like social media and surveys. |
Keywords
* Artificial intelligence * Clustering
