Summary of Reproduction Of Ivfs Algorithm For High-dimensional Topology Preservation Feature Selection, by Zihan Wang
Reproduction of IVFS algorithm for high-dimensional topology preservation feature selection
by Zihan Wang
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 medium-difficulty summary focuses on reproducing the Invariant Vectorial Feature Selection (IVFS) algorithm introduced at AAAI 2020. Inspired by the random subset method, IVFS preserves data similarity while maintaining topological structure. The paper systematically organizes the mathematical foundations of IVFS and validates its effectiveness through numerical experiments similar to those in the original paper. IVFS is compared to SPEC and MCFS on various datasets, showing superior performance on most datasets. However, issues with convergence and stability persist. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This low-difficulty summary explains that researchers found a way to better handle large amounts of data by keeping the important patterns intact. They took inspiration from an old idea called random subset method and created a new algorithm called IVFS. This algorithm helps keep the connections between different pieces of data, which is important for understanding how it all relates. The authors tested their new algorithm on many datasets and found that it did better than other methods in most cases. However, there are still some problems with getting the algorithm to work smoothly. |
Keywords
» Artificial intelligence » Feature selection
