A Large Dimensional Analysis of Multi-task Semi-Supervised Learning
Victor Leger Romain Couillet
This article conducts a large dimensional study of a simple yet quite
versatile classification model, encompassing at once multi-task and
semi-supervised learning, and taking into account uncertain labeling. Using
tools from random matrix theory, we characterize the asymptotics of some key
functionals, which allows us on the one hand to predict the performances of the
algorithm, and on the other hand to reveal some counter-intuitive guidance on
how to use it efficiently. The model, powerful enough to provide good
performance guarantees, is also straightforward enough to provide strong
insights into its behavior.