通过混合子集选择进行降维的监督式方法。
A supervised take on dimensionality reduction via hybrid subset selection.
作者信息
Rahimikollu Javad, Das Jishnu
机构信息
CMU-Pitt Program in Computational Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
出版信息
Patterns (N Y). 2022 Aug 12;3(8):100563. doi: 10.1016/j.patter.2022.100563.
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.
阿莫兹加尔等人提出了HSS-LDA，这是一种用于单细胞数据的有监督降维方法，其性能优于现有的无监督技术。他们将混合子集选择与线性判别分析相结合，并识别出能最佳区分预定义生物组的预测变量的可解释线性组合。
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