Complexity-Adaptive Gaussian Process Model Inference for Large-Scale Data
Fabian Berns, Christian Beecks · Society for Industrial and Applied Mathematics eBooks · 2021
A flexible, domain-agnostic function approximator, which is robust towards unreliable, noisy and partially missing data, would be an ideal tool for pattern mining in large-scale data. Although Gaussian Process Models (GPMs), which are widely regarded as a probabilistic tool for capturing inherent data characteristics, satisfy those requirements, full Gaussian Process inference and training is limited to a few thousand data records. Moreover, a process of automatic GPM inference is required to find an optimal model for a given dataset, despite prevailing default instantiations and existing prior knowledge in some scenarios, which both shortcut the way to an optimal GPM. Since non-approximate Gaussian Processes only allow for processing small datasets with low local statistical versatility, we propose a new approach that enables to automatically infer GPMs of adaptive local complexity on large scale multivariate data. The resulting model is composed of independent statistical representations for disjoint partitions varying in statistical versatility. Our performance evaluation indicates an improvement in inference runtime, while maintaining high model quality with regards to state-of-the-art GPM inference algorithms.