Summary of Density-based Isometric Mapping, by Bardia Yousefi et al.
Density-based Isometric Mapping
by Bardia Yousefi, Mélina Khansari, Ryan Trask, Patrick Tallon, Carina Carino, Arman Afrasiyabi, Vikas Kundra, Lan Ma, Lei Ren, Keyvan Farahani, Michelle Hershman
First submitted to arxiv on: 4 Mar 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 isometric mapping method, known as Isomap, estimates Euclidean distances on High dimensional (HD) manifolds using a shortest path algorithm. However, this approach may lead to inconsistencies between local and global distances when dealing with weakly uniformed HD data. To address this issue, the researchers modified the algorithm by adding a novel constraint inspired by the Parzen-Rosenblatt (PR) window, which maintains the uniformity of the constructed shortest-path graph in Isomap. The proposed method, called PR-Isomap, was benchmarked and validated using multiple imaging datasets, including 72,236 cases from various modalities. The results showed that PR-Isomap projects HD attributes into a lower-dimensional (LD) space while preserving information, achieving high comparative accuracies for pneumonia diagnosis and overall survival prediction in NSCLC patients. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to map complex data onto a simpler one using an algorithm called Isomap. The problem with the original Isomap method is that it can make mistakes when dealing with certain types of data. To fix this, the researchers came up with a new idea inspired by something called the Parzen-Rosenblatt window. They tested their new method, called PR-Isomap, using lots of different images and medical datasets. The results show that PR-Isomap works better than the old way at predicting things like whether someone has pneumonia or is likely to survive. |
