A supervised take on dimensionality reduction via hybrid subset selection

Patterns (N Y). 2022 Aug 12;3(8):100563. doi: 10.1016/j.patter.2022.100563.

Abstract

Amouzgar et al. present HSS-LDA, a supervised dimensionality reduction approach for single-cell data that outperforms existing unsupervised techniques. They couple hybrid subset selection to linear discriminant analysis and identify interpretable linear combinations of predictors that best separate predefined biological groups.

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