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.
Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices.The Annals of Probability, 33, 2005
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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.