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The joint analysis of biomedical data in Alzheimer's Disease (AD) isimportant for better clinical diagnosis and to understand the relationshipbetween biomarkers. However, jointly accounting for heterogeneous measuresposes important challenges related to the modeling of the variability and theinterpretability of the results. These issues are here addressed by proposing anovel multi-channel stochastic generative model. We assume that a latentvariable generates the data observed through different channels (e.g., clinicalscores, imaging, ...) and describe an efficient way to estimate jointly thedistribution of both latent variable and data generative process. Experimentson synthetic data show that the multi-channel formulation allows superior datareconstruction as opposed to the single channel one. Moreover, the derivedlower bound of the model evidence represents a promising model selectioncriterion. Experiments on AD data show that the model parameters can be usedfor unsupervised patient stratification and for the joint interpretation of theheterogeneous observations. Because of its general and flexible formulation, webelieve that the proposed method can find important applications as a generaldata fusion technique.

Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease