Parallelizable sparse inverse formulation Gaussian processes (SpInGP)

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 i...

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Bibliographic Details
Published in2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) pp. 1 - 6
Main Authors Grigorievskiy, Alexander, Lawrence, Neil, Sarkka, Simo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2017
Subjects
Computational modeling
Covariance matrices
Gaussian processes
Inference algorithms
Sparse matrices
Time complexity
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Summary: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.
DOI:10.1109/MLSP.2017.8168130