Summary of Rectified Gaussian Kernel Multi-view K-means Clustering, by Kristina P. Sinaga
Rectified Gaussian kernel multi-view k-means clustering
by Kristina P. Sinaga
First submitted to arxiv on: 9 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 authors propose two new variants of multi-view k-means algorithms to address multi-view data. The first variant, multi-view k-means with exponent distance (MVKM-ED), uses the Euclidean norm in the space of Gaussian-kernel to calculate similarity between data points and cluster centers. This approach learns multi-view data by simultaneously aligning stabilizer parameters and kernel coefficients. The second variant, Gaussian-kernel multi-view k-means (GKMVKM) clustering algorithm, further compresses Gaussian-kernel based weighted distance in Euclidean norm to reduce sensitivity. Numerical evaluation on five real-world datasets demonstrates the robustness and efficiency of these proposed approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops new algorithms for processing data from multiple views or sources. These algorithms help group similar data points together by using a special type of math called Gaussian-kernel. This approach is useful because it can handle different types of data and make sure that important patterns are not missed. The authors tested their algorithms on real-world data and found that they work well. |
Keywords
» Artificial intelligence » Clustering » K means
