Summary of Improved Algorithm and Bounds For Successive Projection, by Jiashun Jin et al.
Improved Algorithm and Bounds for Successive Projection
by Jiashun Jin, Zheng Tracy Ke, Gabriel Moryoussef, Jiajun Tang, Jingming Wang
First submitted to arxiv on: 16 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST)
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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 new algorithm called pseudo-point successive projection algorithm (pp-SPA) for vertex hunting in high-dimensional spaces. Vertex hunting involves estimating the vertices of a K-simplex from noisy measurements of n points on or outside the simplex. The authors aim to improve upon existing algorithms, specifically the successive projection algorithm (SPA), which is known to perform poorly under strong noise or outliers. They derive error bounds for pp-SPA using extreme value theory and show that it has faster rates and better numerical performances than SPA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists try to find the points that make up a special shape in space called a simplex. But they only get to see some of the points, and those points might be wrong because of noise or mistakes. They want to come up with a new way to figure out where the real points are, so they can learn more about the shape. They compare their new method to an old one that doesn’t work very well in these situations. Their results show that their new method is better and helps them understand the shape of things. |
