In high-dimensional analysis, pretrained PCA representations for linear probing generalize best at low dimensionality when pretraining data is plentiful but labeled data scarce, with an exact trade-off showing how much unlabeled data replaces one labeled sample.
Reconciling modern machine-learning practice and the classical bias–variance trade-off.Proceedings of the National Academy of Sciences, 116
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Optimal Representation Size: High-Dimensional Analysis of Pretraining and Linear Probing
In high-dimensional analysis, pretrained PCA representations for linear probing generalize best at low dimensionality when pretraining data is plentiful but labeled data scarce, with an exact trade-off showing how much unlabeled data replaces one labeled sample.