Manifold Relevance Determination

Understand

In this paper we present a fully Bayesian latent variable model which exploits conditional nonlinear(in)-dependence structures to learn an efficient latent representation.

• The latent space is factorized to represent shared and private information from multiple views of the data.
• In contrast to previous approaches, we introduce a relaxation to the discrete segmentation and allow for a "softly" shared latent space.
• Further, Bayesian techniques allow us to automatically estimate the dimensionality of the latent spaces.