  Gaussian process latent variable models (GPLVM) are a flexible and non-linear
approach to dimensionality reduction, extending classical Gaussian processes to
an unsupervised learning context. The Bayesian incarnation of the GPLVM Titsias
and Lawrence, 2010] uses a variational framework, where the posterior over
latent variables is approximated by a well-behaved variational family, a
factorized Gaussian yielding a tractable lower bound. However, the
non-factories ability of the lower bound prevents truly scalable inference. In
this work, we study the doubly stochastic formulation of the Bayesian GPLVM
model amenable with minibatch training. We show how this framework is
compatible with different latent variable formulations and perform experiments
to compare a suite of models. Further, we demonstrate how we can train in the
presence of massively missing data and obtain high-fidelity reconstructions. We
demonstrate the model's performance by benchmarking against the canonical
sparse GPLVM for high-dimensional data examples.
