Multidataset Independent Subspace Analysis With Application to Multimodal Fusion.

ABSTRACT: Unsupervised latent variable models-blind source separation (BSS) especially-enjoy a strong reputation for their interpretability. But they seldom combine the rich diversity of information available in multiple datasets, even though multidatasets yield insightful joint solutions otherwise unavailable in isolation. We present a direct, principled approach to multidataset combination that takes advantage of multidimensional subspace structures. In turn, we extend BSS models to capture the underlying modes of shared and unique variability across and within datasets. Our approach leverages joint information from heterogeneous datasets in a flexible and synergistic fashion. We call this method multidataset independent subspace analysis (MISA). Methodological innovations exploiting the Kotz dist

SUBMITTER: Silva RF 

PROVIDER: S-EPMC7877797 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature