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Independent component analysis (ICA) is a blind source separation method forlinear disentanglement of independent latent sources from observed data. Weinvestigate the special setting of noisy linear ICA where the observations aresplit among different views, each receiving a mixture of shared and individualsources. We prove that the corresponding linear structure is identifiable, andthe source distribution can be recovered. To computationally estimate thesources, we optimize a constrained form of the joint log-likelihood of theobserved data among all views. We also show empirically that our objectiverecovers the sources also in the case when the measurements are corrupted bynoise. Furthermore, we propose a model selection procedure for recovering thenumber of shared sources which we verify empirically. Finally, we apply theproposed model in a challenging real-life application, where the estimatedshared sources from two large transcriptome datasets (observed data) providedby two different labs (two different views) lead to recovering (shared) sourcesutilized for finding a plausible representation of the underlying graphstructure.

Multi-View Independent Component Analysis with Shared and Individual Sources