  This paper addresses the problem of learning dictionaries for multimodal
datasets, i.e. datasets collected from multiple data sources. We present an
algorithm called multimodal sparse Bayesian dictionary learning (MSBDL). MSBDL
leverages information from all available data modalities through a joint
sparsity constraint. The underlying framework offers a considerable amount of
flexibility to practitioners and addresses many of the shortcomings of existing
multimodal dictionary learning approaches. In particular, the procedure
includes the automatic tuning of hyperparameters and is unique in that it
allows the dictionaries for each data modality to have different cardinality, a
significant feature in cases when the dimensionality of data differs across
modalities. MSBDL is scalable and can be used in supervised learning settings.
Theoretical results relating to the convergence of MSBDL are presented and the
numerical results provide evidence of the superior performance of MSBDL on
synthetic and real datasets compared to existing methods.
