Summary of Multiple Kernel Clustering Via Local Regression Integration, by Liang Du et al.
Multiple Kernel Clustering via Local Regression Integration
by Liang Du, Xin Ren, Haiying Zhang, Peng Zhou
First submitted to arxiv on: 20 Oct 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 A novel approach to multiple kernel clustering, known as CMKLR, is introduced in this paper. The traditional multiple kernel methods do not consider the intrinsic manifold structure of the data and require a quadratic number of variables, making them vulnerable to noise and outliers. To address these limitations, the authors propose CKLR, which captures the local structure of the kernel data using kernelized local regression. This method is then extended to CMKLR, which characterizes the kernel level manifold structure by constructing a sparse coefficient matrix for each candidate kernel. The proposed method reduces the number of variables and becomes more robust against noise and outliers. Experimental results show that CMKLR outperforms 10 state-of-the-art multiple kernel clustering methods on benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to group similar things together using multiple types of data. Current methods have limitations, like being sensitive to errors in the data. The authors develop two new approaches: CKLR and CMKLR. These methods use local regression to capture patterns in the data and reduce the impact of errors. They show that their method is better than 10 other popular methods at grouping similar things together. |
Keywords
» Artificial intelligence » Clustering » Regression
