cpfa: Classification with Parallel Factor Analysis
Classification using Richard A. Harshman's Parallel Factor Analysis-1 (Parafac) model or Parallel Factor Analysis-2 (Parafac2) model fit to a three-way or four-way data array/tensor. See Harshman and Lundy (1994): <doi:10.1016/0167-9473(94)90132-5>. Uses component weights from one mode of the Parafac or Parafac2 model as features to tune parameters for one or more classification methods via a k-fold cross-validation procedure. Supports penalized logistic regression, support vector machine, random forest, and feed-forward neural network. Supports binary and multiclass classification. Predicts class labels or class probabilities and calculates multiple classification performance measures. Parallel computing is implemented via the 'parallel' and 'doParallel' packages.
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