Summary of Multi-view Clustering Via Unified Multi-kernel Learning and Matrix Factorization, by Chenxing Jia et al.
Multi-view Clustering via Unified Multi-kernel Learning and Matrix Factorization
by Chenxing Jia, Mingjie Cai, Hamido Fujita
First submitted to arxiv on: 12 Dec 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 proposes a novel multi-view clustering method that integrates multi-kernel learning with matrix factorization, addressing limitations in existing approaches. The new approach combines the strengths of both methods while removing orthogonal constraints on individual views and imposing them on the consensus matrix. This results in an accurate final clustering structure. The method is unified into a simple form of multi-kernel clustering, reducing computational complexity by avoiding the need to learn an optimal kernel. An efficient three-step optimization algorithm is also proposed to achieve a locally optimal solution. Experimental results on real-world datasets demonstrate the effectiveness of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to group similar data from multiple sources together. It combines two existing techniques, multi-kernel learning and matrix factorization, to make it more accurate and efficient. The new approach is better than previous methods because it doesn’t require learning an optimal kernel, which makes it faster. The authors also developed a simple optimization algorithm to help the method work better. They tested the new approach on real-world data sets and showed that it works well. |
Keywords
» Artificial intelligence » Clustering » Optimization
