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23 Aug 2022
In contemporary machine learning, it is critical to identify ?informative? low-dimensional features from high-dimensional data for learning tasks, while the notion of ?informative? is not unified over learning problems. In this tutorial, we introduce a natural information metric for quantifying the informativeness of features from Hirschfeld-Gebelein-R‚nyi (HGR) maximal correlation and a modal decomposition of the dependence between random variables, via a local
