Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes

Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019)

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Rui Li


This paper studies statistical characteristics of multivariate observations with irregular changes in their covariance structures across input space. We propose a unified nonstationary modeling framework to jointly encode the observation correlations to generate a piece-wise representation with a hyper-level Gaussian process (GP) governing the overall contour of the pieces. In particular, we couple the encoding process with automatic relevance determination (ARD) to promote sparsity to account for the inherent redundancy. The hyper GP enables us to share statistical strength among the observation variables over a collection of GPs defined within the observation pieces to characterize the variables' respective local smoothness. Experiments conducted across domains show superior performances over the state-of-the-art methods.