Adding PCA factors extracted from transformed input matrices (interactions, kernels, neural network hidden layers) to the original features improves out-of-sample prediction in many high-dimensional learning tasks.
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Feature Augmentations for High-Dimensional Learning
Adding PCA factors extracted from transformed input matrices (interactions, kernels, neural network hidden layers) to the original features improves out-of-sample prediction in many high-dimensional learning tasks.