Automated Model Inference for Gaussian Processes: An Overview of State-of-the-Art Methods and Algorithms

Gaussian process models (GPMs) are widely regarded as a prominent tool for learning statistical data models that enable interpolation, regression, and classification. These models are typically instantiated by a Gaussian Process with a zero-mean function and a radial basis covariance function. While...

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Vydáno v:	SN computer science Ročník 3; číslo 4; s. 300
Hlavní autoři:	Berns, Fabian, Hüwel, Jan, Beecks, Christian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Singapore Springer Nature Singapore 01.07.2022 Springer Nature B.V
Témata:	Algorithms Approximation Automation Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Datasets Empirical analysis Gaussian process Inference Information Systems and Communication Service Interpolation Knowledge Discovery Knowledge Engineering and Knowledge Management Model accuracy Pattern Recognition and Graphics Random variables Review Review Article Software Engineering/Programming and Operating Systems State-of-the-art reviews Statistical analysis Vision Gaussian processes Machine learning Probabilistic machine learning
ISSN:	2661-8907, 2662-995X, 2661-8907
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Popis
Shrnutí:	Gaussian process models (GPMs) are widely regarded as a prominent tool for learning statistical data models that enable interpolation, regression, and classification. These models are typically instantiated by a Gaussian Process with a zero-mean function and a radial basis covariance function. While these default instantiations yield acceptable analytical quality in terms of model accuracy, GPM inference algorithms automatically search for an application-specific model fitting a particular dataset. State-of-the-art methods for automated inference of GPMs are searching the space of possible models in a rather intricate way and thus result in super-quadratic computation time complexity for model selection and evaluation. Since these properties only enable processing small datasets with low statistical versatility, various methods and algorithms using global as well as local approximations have been proposed for efficient inference of large-scale GPMs. While the latter approximation relies on representing data via local sub-models, global approaches capture data’s inherent characteristics by means of an educated sample. In this paper, we investigate the current state-of-the-art in automated model inference for Gaussian processes and outline strengths and shortcomings of the respective approaches. A performance analysis backs our theoretical findings and provides further empirical evidence. It indicates that approximated inference algorithms, especially locally approximating ones, deliver superior runtime performance, while maintaining the quality level of those using non-approximative Gaussian processes.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23
ISSN:	2661-8907 2662-995X 2661-8907
DOI:	10.1007/s42979-022-01186-x