Cersonsky, Rose K and Helfrecht, Benjamin A and Engel, Edgar A and Kliavinek, Sergei and Ceriotti, Michele (2021) Improving sample and feature selection with principal covariates regression. Machine Learning: Science and Technology, 2 (3). 035038. ISSN 2632-2153
![[thumbnail of Cersonsky_2021_Mach._Learn.__Sci._Technol._2_035038.pdf]](http://asian.openbookpublished.com/style/images/fileicons/text.png)
Cersonsky_2021_Mach._Learn.__Sci._Technol._2_035038.pdf - Published Version
Download (1MB)
Abstract
Selecting 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.
Item Type: | Article |
---|---|
Subjects: | Open STM Article > Multidisciplinary |
Depositing User: | Unnamed user with email support@openstmarticle.com |
Date Deposited: | 04 Jul 2023 04:22 |
Last Modified: | 26 Jun 2024 10:38 |
URI: | http://asian.openbookpublished.com/id/eprint/1252 |