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Domain-aware Gaussian process state-space models
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DOI：10.1016/j.sigpro.2025.110003.png)
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Abstract
En 中文
• We introduce Domain-Aware Gaussian Process State-Space Model (DA-GPSSM), based on Reduced-Rank Gaussian Process State-Space Model (RR-GPSSMs), to explicitly model the relationship between different state-dimension. • Our proposed framework significantly reduces the computational load to up to several orders of magnitude from O(M3D) for a D dimensional state-space model to O(M3K), where K≤D depending on the application domain. • We show through simulations the comparable performance of DA-GPSSM w.r.t. the state of the methods, and the significant improvement in computational complexity for multiple applications.
Keywords:
Gaussian processes
Non-parametric learning
Dynamical systems
State-space models
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