Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption

Vasilii Feofanov, Malik Tiomoko, Aladin Virmaux

We propose a theoretical framework to analyze semi-supervised classification under the low density separation assumption in a high-dimensional regime. In particular, we introduce Q…