Unsupervised discovery of the shared and private geometry in multi-view data
This addresses the need for better tools in neuroscience and other fields to understand nonlinear relationships in multi-view data, though it is an incremental improvement over prior methods.
The paper tackled the problem of analyzing multi-view data, such as neural recordings from different brain regions, by developing SPLICE, a neural network method that disentangles shared and private latent variables, resulting in more effective disentanglement, interpretable geometry-preserving representations, and robustness to dimensionality errors compared to existing methods.
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.