Title: Unsupervised discovery of the shared and private geometry in multi-view data
Primary area: applications to neuroscience & cognitive science
Keywords: manifold learning, multi-view, neuroscience, neural network, multi-region

Abstract: Studying complex real-world phenomena often involves data from multiple views (e.g. sensor modalities or brain regions), each capturing different
	aspects of the underlying system. Within neuroscience, there
	is growing interest in large-scale simultaneous recordings across multiple
	brain regions. Understanding the relationship between views (e.g., the neural
	activity in each region recorded) can reveal fundamental insights
	into each view and the system as a whole. However, existing methods to
	characterize such relationships lack the expressivity required to
	capture nonlinear relationships, describe only shared sources
	of variance, or discard geometric information
	that is crucial to drawing insights from data. Here, we present SPLICE: a neural network-based method that infers disentangled,
	interpretable representations of private and shared latent variables from
	paired samples of high-dimensional views. Compared to competing methods, we
demonstrate that SPLICE **1)** disentangles shared and private
representations more effectively, **2)** yields more interpretable
representations by preserving geometry, and **3)** is more robust to
incorrect a priori estimates of latent dimensionality. We propose our approach as a general-purpose
method for finding succinct and interpretable descriptions of paired data
sets in terms of disentangled shared and private latent variables.

TL;DR: We develop a novel method for learning geometry-preserving representations of shared and private information from multiview data, demonstrating superior interpretability and disentangling than existing methods.