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.
| Version: |
1.0.1 |
| Depends: |
R (≥ 3.1.1) |
| Imports: |
tensr, Matrix, MASS, methods |
| Published: |
2021-01-04 |
| Author: |
Yuqing Pan,
Qing Mai,
Xin Zhang |
| Maintainer: |
Yuqing Pan <yuqing.pan at stat.fsu.edu> |
| License: |
GPL-2 |
| NeedsCompilation: |
yes |
| CRAN checks: |
catch results |
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