Summary of Consistent Spectral Clustering in Hyperbolic Spaces, by Sagar Ghosh and Swagatam Das
Consistent Spectral Clustering in Hyperbolic Spaces
by Sagar Ghosh, Swagatam Das
First submitted to arxiv on: 14 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 proposed spectral clustering algorithm on Hyperbolic Spaces addresses the limitations of traditional clustering techniques by efficiently representing complex data structures like hierarchical and tree-like structures. Building upon recent advancements in Deep Neural Networks on hyperbolic spaces, this work develops a novel clustering algorithm that replaces the Euclidean Similarity Matrix with an appropriate Hyperbolic Similarity Matrix. The algorithm demonstrates improved efficiency compared to clustering in Euclidean Spaces, with experimental results on the Wisconsin Breast Cancer Dataset showcasing its superior performance over traditional Spectral Clustering. This contribution opens up new avenues for utilizing non-Euclidean Spaces in clustering algorithms, offering improved clustering efficiency and handling complex data structures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to group similar things together (clustering) that works well with complex data structures like trees or hierarchies. Right now, most clustering methods use Euclidean Spaces, but they can be limited when dealing with complex data. The researchers propose a new algorithm that uses Hyperbolic Spaces instead, which are better suited for representing these types of data. They show that their method is more efficient and effective than traditional methods on certain datasets. |
Keywords
» Artificial intelligence » Clustering » Spectral clustering
