Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov Approximation

17 Nov 2014 Kian Hsiang Low Jiangbo Yu Jie Chen Patrick Jaillet

The expressive power of a Gaussian process (GP) model comes at a cost of poor scalability in the data size. To improve its scalability, this paper presents a low-rank-cum-Markov approximation (LMA) of the GP model that is novel in leveraging the dual computational advantages stemming from complementing a low-rank approximate representation of the full-rank GP based on a support set of inputs with a Markov approximation of the resulting residual process; the latter approximation is guaranteed to be closest in the Kullback-Leibler distance criterion subject to some constraint and is considerably more refined than that of existing sparse GP models utilizing low-rank representations due to its more relaxed conditional independence assumption (especially with larger data)... (read more)

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Gaussian Process
Non-Parametric Classification