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Improving sample and feature selection with principal covariates regression

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jats:titleAbstract</jats:title> jats:pSelecting the most relevant features and samples out of a large set of candidates is a task that occurs very often in the context of automated data analysis, where it improves the computational performance and often the transferability of a model. Here we focus on two popular subselection schemes applied to this end: CUR decomposition, derived from a low-rank approximation of the feature matrix, and farthest point sampling (FPS), which relies on the iterative identification of the most diverse samples and discriminating features. We modify these unsupervised approaches, incorporating a supervised component following the same spirit as the principal covariates (PCov) regression method. We show how this results in selections that perform better in supervised tasks, demonstrating with models of increasing complexity, from ridge regression to kernel ridge regression and finally feed-forward neural networks. We also present adjustments to minimise the impact of any subselection when performing unsupervised tasks. We demonstrate the significant improvements associated with PCov-CUR and PCov-FPS selections for applications to chemistry and materials science, typically reducing by a factor of two the number of features and samples required to achieve a given level of regression accuracy.</jats:p>


Funder: Trinity College, University of Cambridge; doi:


46 Information and Computing Sciences, 4611 Machine Learning, Generic health relevance

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Machine Learning: Science and Technology

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IOP Publishing
H2020 European Research Council (677013-HBMAP)
Swiss National Supercomputing Centre (s1000, s960)