Summary of Fast Asymmetric Factorization For Large Scale Multiple Kernel Clustering, by Yan Chen et al.
Fast Asymmetric Factorization for Large Scale Multiple Kernel Clustering
by Yan Chen, Liang Du, Lei Duan
First submitted to arxiv on: 26 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 The paper proposes Efficient Multiple Kernel Concept Factorization (EMKCF), a novel approach for large-scale Multiple Kernel Clustering (MKC) that addresses memory and time constraints by constructing a sparse kernel matrix inspired by local regression. EMKCF extends orthogonal concept factorization to handle multiple kernels, allowing for efficient learning of consensus and individual representations. The method is compared to state-of-the-art approaches on benchmark datasets, demonstrating its effectiveness in terms of efficiency and performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to group similar things together (clustering) using information from many different sources (kernels). This helps solve a problem with earlier methods that combined all the information at once or one piece of information at a time. The new approach, called Efficient Multiple Kernel Concept Factorization (EMKCF), is faster and more efficient than previous methods and works well on big datasets. |
Keywords
» Artificial intelligence » Clustering » Regression
