The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA

Understand

We consider the problem of recovering a common latent source with independent components from multiple views.

• This applies to settings in which a variable is measured with multiple experimental modalities, and where the goal is to synthesize the disparate measurements into a single unified representation.
• We consider the case that the observed views are a nonlinear mixing of component-wise corruptions of the sources.
• When the views are considered separately, this reduces to nonlinear Independent Component Analysis (ICA) for which it is provably impossible to undo the mixing.