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) . 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.


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1.0.1 by Yuqing Pan, a year ago

Browse source code at

Authors: Yuqing Pan <[email protected]> , Qing Mai <[email protected]> , Xin Zhang <[email protected]>

Documentation:   PDF Manual  

GPL-2 license

Imports tensr, Matrix, MASS, methods

See at CRAN