Output-Dependent Gaussian Process State-Space Model

• URL: http://arxiv.org/abs/2212.07608v1
• Date: Thu, 15 Dec 2022 04:05:39 GMT
• Title: Output-Dependent Gaussian Process State-Space Model
• Authors: Zhidi Lin, Lei Cheng, Feng Yin, Lexi Xu, Shuguang Cui
• Abstract summary: This paper proposes an output-dependent and more realistic GPSSM. Experiments on both synthetic and real datasets demonstrate the superiority of the output-dependent GPSSM in terms of learning and inference performance.
• Score: 34.34146690947695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gaussian process state-space model (GPSSM) is a fully probabilistic state-space model that has attracted much attention over the past decade. However, the outputs of the transition function in the existing GPSSMs are assumed to be independent, meaning that the GPSSMs cannot exploit the inductive biases between different outputs and lose certain model capacities. To address this issue, this paper proposes an output-dependent and more realistic GPSSM by utilizing the well-known, simple yet practical linear model of coregionalization (LMC) framework to represent the output dependency. To jointly learn the output-dependent GPSSM and infer the latent states, we propose a variational sparse GP-based learning method that only gently increases the computational complexity. Experiments on both synthetic and real datasets demonstrate the superiority of the output-dependent GPSSM in terms of learning and inference performance.

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