Summary of Ivfs: Simple and Efficient Feature Selection For High Dimensional Topology Preservation, by Xiaoyun Li et al.
IVFS: Simple and Efficient Feature Selection for High Dimensional Topology Preservation
by Xiaoyun Li, Chengxi Wu, Ping Li
First submitted to arxiv on: 2 Apr 2020
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 paper proposes a novel feature selection algorithm that preserves sample similarity and topological patterns in high-dimensional data. Building on the unified framework of IVFS, inspired by random subset methods, the algorithm uses persistent diagrams from computational topology to enhance topology preservation. This approach can handle analytically intractable problems and efficiently handles large-scale datasets. The proposed method demonstrates good performance under sub-sampling rates, making it a promising tool for dealing with high-dimensional data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to make sense of very big datasets. When we have too many features or variables, it can be hard to understand what’s important and what’s not. The authors propose a new way to choose the most important features that keeps the same patterns and relationships between data points as the original dataset had. This method is helpful for working with large datasets because it works well even when we don’t have enough information. |
Keywords
* Artificial intelligence * Feature selection
