Paper
Markov Chain Gradient Descent in Hilbert Spaces
Authors
Priyanka Roy, Susanne Saminger-Platz
Abstract
In this paper, we study a Markov chain-based stochastic gradient algorithm in general Hilbert spaces, aiming at approximating the optimal solution of a quadratic loss function. We establish probabilistic upper bounds on its convergence. We further extend these results to an online regularized learning algorithm in reproducing kernel Hilbert spaces, where the samples are drawn along a Markov chain trajectory.