Sparse Linear Discriminant Analysis using the Prior-Knowledge-Guided Block Covariance Matrix.

ABSTRACT: There are two key challenges when using a linear discriminant analysis in the high-dimensional setting, including singularity of the covariance matrix and difficulty of interpreting the resulting classifier. Although several methods have been proposed to address these problems, they focused only on identifying a parsimonious set of variables maximizing classification accuracy. However, most methods did not consider dependency between variables and efficacy of selected variables appropriately. To address these limitations, here we propose a new approach that directly estimates the sparse discriminant vector without a need of estimating the whole inverse covariance matrix, by formulating a quadratic optimization problem. Furthermore, this approach also allows to integrate external informatio

SUBMITTER: Nam JH 

PROVIDER: S-EPMC7505231 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature