One-class classification with Gaussian processes

摘要

Detecting instances of unknown categories is an important task for a multitude of problems such as object recognition, event detection, and defect localization. This article investigates the use of Gaussian process (GP) priors for this area of research. Focusing on the task of one-class classification, we analyze different measures derived from GP regression and approximate GP classification. We also study important theoretical connections to other approaches and discuss their underlying assumptions. Experiments are performed using a large number of datasets and different image kernel functions. Our findings show that our approaches can outperform the well-known support vector data description approach indicating the high potential of Gaussian processes for one-class classification. Furthermore, we show the suitability of our methods in the area of attribute prediction, defect localization, bacteria recognition, and background subtraction. These applications and experiments highlight the easy applicability of our method as well as its state-of-the-art performance compared to established methods.