catch: Covariate-Adjusted Tensor Classification in High-Dimensions

Performs classification and variable selection on high-dimensional tensors (multi-dimensional arrays) after adjusting for additional covariates (scalar or vectors) as CATCH model in Pan, Mai and Zhang (2018) <arXiv:1805.04421>. The low-dimensional covariates and the high-dimensional tensors are jointly modeled to predict a categorical outcome in a multi-class discriminant analysis setting. The Covariate-Adjusted Tensor Classification in High-dimensions (CATCH) model is fitted in two steps: (1) adjust for the covariates within each class; and (2) penalized estimation with the adjusted tensor using a cyclic block coordinate descent algorithm. The package can provide a solution path for tuning parameter in the penalized estimation step. Special case of the CATCH model includes linear discriminant analysis model and matrix (or tensor) discriminant analysis without covariates.

Getting started

Package details

AuthorYuqing Pan <[email protected]>, Qing Mai <[email protected]>, Xin Zhang <[email protected]>
MaintainerYuqing Pan <[email protected]>
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the catch package in your browser

Any scripts or data that you put into this service are public.

catch documentation built on May 14, 2018, 5:08 p.m.