2017/02/15 by Matthew W. Wheeler, Wheeler, Matthew W. · 1 citation
Computer Science · Engineering · Mathematics · #Asphalt Pavement Performance Evaluation #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Machine Learning and Data Classification #stat.ML
paper · pdf · doi:10.48550/arxiv.1702.04775
openalex publication_date 2017/02/15 · arxiv created 2017/06/15 · arxiv updated 2017/06/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many modern data sets are sampled with error from complex high-dimensional surfaces. Methods such as tensor product splines or Gaussian processes are effective/well suited for characterizing a surface in two or three dimensions but may suffer from difficulties when representing higher dimensional surfaces. Motivated by high throughput toxicity testing where observed dose-response curves are cross sections of a surface defined by a chemical's structural properties, a model is developed to characterize this surface to predict untested chemicals' dose-responses. This manuscript proposes a novel approach that models the multidimensional surface as a sum of learned basis functions formed as the tensor product of lower dimensional functions, which are themselves representable by a basis expansion learned from the data. The model is described, a Gibbs sampling algorithm proposed, and is investigated in a simulation study as well as data taken from the US EPA's ToxCast high throughput toxicity testing platform.