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.
On sample eigenvalues in a generalized spiked population model.Journal of Multivariate Analysis, 106, 2012
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.