Summary of Deep Matrix Factorization with Adaptive Weights For Multi-view Clustering, by Yasser Khalafaoui (alteca) et al.
Deep Matrix Factorization with Adaptive Weights for Multi-View Clustering
by Yasser Khalafaoui, Basarab Matei, Martino Lovisetto, Nistor Grozavu
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 matrix factorization model called DMFAW for unsupervised multi-view clustering tasks. The method simultaneously incorporates feature selection and generates local partitions, which enhances the clustering results. A control theory-inspired mechanism dynamically updates the features weights to improve stability, adaptability, and convergence speed. The optimization problem is solved via an alternating optimization algorithm with guaranteed convergence. Experimental results on benchmark datasets demonstrate that DMFAW outperforms state-of-the-art methods in terms of clustering performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to group similar things together called multi-view clustering using a special kind of AI model called deep matrix factorization. The old ways of doing this didn’t do a great job, so the authors came up with a new method that helps choose which features are most important and groups them into categories. This makes the results better and faster. They tested it on some big datasets and showed that it works really well. |
Keywords
» Artificial intelligence » Clustering » Feature selection » Optimization » Unsupervised
