Berns, F.F.BernsSchmidt, K.K.SchmidtBracht, I.I.BrachtBeecks, C.C.Beecks2022-03-152022-03-152021https://publica.fraunhofer.de/handle/publica/41196810.1109/ICPR48806.2021.9412805Gaussian Process Models (GPMs) are Bayesian machine learning models that have been widely applied in the domain of pattern recognition due to their ability to infer from unreliable, noisy, or highly idiosyncratic data. Retrieving a complex GPM describing the data's inherent statistical patterns, such as trends, seasonalities, and periodicities, is a key requirement for various pattern recognition tasks. In this paper, we propose a novel approach for efficient large-scale GPM retrieval: the Concatenated Composite Covariance Search (3CS) algorithm. By making use of multiple local kernel searches on dynamically partitioned data, the 3CS algorithm is able to overcome the performance limitations of state-of-the-art GPM retrieval algorithms and to efficiently retrieve GPMs for large-scale data up to three orders of magnitude as fast as state-of-the-art algorithms.en004005006conference paper