An efficient and accurate numerical determination of the cluster resolution metric in two dimensions.

Cluster resolution (CR) is a useful metric for guiding automated feature selection of classification models. CR is a measure of class separation in a linear subspace for variable subsets via the determination of maximal, non‐intersecting confidence ellipses. Feature selection by cluster resolution (FS‐CR) is most commonly used to extract panels of useful, discriminating features from sparsely populated chromatographic peak tables, optimizing models from raw signals, or when working with datasets with many more variables than samples. The absence of a numerical method for calculating CR necessitates a great deal of dynamic programming and algorithmic complexity. In this work, we present a numerical determination of the CR metric, which reduces computation time by about 65 times when compared with the dynamic programming approach and simplifies the operating principles of FS‐CR algorithm. Cluster resolution is a measure of class separation in a linear subspace, which has been used to guide feature selection algorithms. In this contribution, a numerical method for calculating cluster resolution is presented. An algorithm implementing the numerical determination of cluster resolution is 65× faster than the current implementation, which relies on dynamic programming. [ABSTRACT FROM AUTHOR]