Package: catch 1.0.1

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.

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

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Last updated 4 years agofrom:6bf45e82ea. Checks:OK: 9. Indexed: yes.

Exports:adjtencatchcatch_matrixcv.catchpredict.catch

Dependencies:assertthatlatticeMASSMatrixtensr