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Ranking the information content of distance measures. (arXiv:2104.15079v2 [stat.ML] UPDATED)
May 27, 2022, 1:11 a.m. | Aldo Glielmo, Claudio Zeni, Bingqing Cheng, Gabor Csanyi, Alessandro Laio
stat.ML updates on arXiv.org arxiv.org
Real-world data typically contain a large number of features that are often
heterogeneous in nature, relevance, and also units of measure. When assessing
the similarity between data points, one can build various distance measures
using subsets of these features. Using the fewest features but still retaining
sufficient information about the system is crucial in many statistical learning
approaches, particularly when data are sparse. We introduce a statistical test
that can assess the relative information retained when using two different
distance …
More from arxiv.org / stat.ML updates on arXiv.org
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