Summary of Beyond the Noise: Intrinsic Dimension Estimation with Optimal Neighbourhood Identification, by Antonio Di Noia et al.
Beyond the noise: intrinsic dimension estimation with optimal neighbourhood identification
by Antonio Di Noia, Iuri Macocco, Aldo Glielmo, Alessandro Laio, Antonietta Mira
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST); Computation (stat.CO); Methodology (stat.ME)
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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 A machine learning protocol is introduced that automatically selects the optimal scale at which the Intrinsic Dimension (ID) is meaningful for unsupervised learning and feature selection tasks. The ID is a lower bound to the number of variables required to describe a system, but its value depends on the analysis scale in real-world datasets. The proposed protocol ensures that the density of data is constant for distances smaller than the correct scale, which allows for self-consistent estimation of the ID. Theoretical guarantees and benchmark results on artificial and real-world datasets demonstrate the usefulness and robustness of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us figure out how to find the right way to measure a set of data points in a way that makes sense. We need to do this because some methods of measuring data don’t work well if we’re looking at it from too close or too far away. The authors came up with a new way to choose the best scale for our measurements, which involves making sure that the density of the data is consistent when we look at it closely. This approach is important and works well on real-world data. |
Keywords
» Artificial intelligence » Feature selection » Machine learning » Unsupervised
