Parallelizable sparse inverse formulation Gaussian processes (SpInGP)

TL;DR

The paper introduces SpInGP, a parallelizable sparse inverse Gaussian process model for temporal data, achieving linear and sublinear computational complexity through sparse matrices and state-space formulation.

Contribution

It presents a novel parallelizable sparse inverse Gaussian process leveraging sparse matrices and state-space models for efficient temporal data analysis.

Findings

Linear time complexity for basic SpInGP.

Sublinear complexity with parallel implementation.

Effective on both simulated and real datasets.

Abstract

We propose a parallelizable sparse inverse formulation Gaussian process (SpInGP) for temporal models. It uses a sparse precision GP formulation and sparse matrix routines to speed up the computations. Due to the state-space formulation used in the algorithm, the time complexity of the basic SpInGP is linear, and because all the computations are parallelizable, the parallel form of the algorithm is sublinear in the number of data points. We provide example algorithms to implement the sparse matrix routines and experimentally test the method using both simulated and real data.