Detecting new classes via infinite warped mixture models for hyperspectral image analysis

Novelty (new class) detection can be described as the identification of new or “unknown” data that a machine learning system was not aware of during training. The ability to detect new classes can have a significant positive impact on image analysis systems, where the test data (or unlabeled data) may contain information about objects that were not known during training process. Since infinite Gaussian mixture models (IGMM) are capable to fit data with an unknown number of mixtures, the inference scheme based on semi-supervised Gibbs sampling can differentiate between known and novel data by learning the unique data clustering in training and testing modes. In order to deal with non-Gaussian (especially heavy tailed) data, the proposed approach is based on infinite warped mixture models (IWMM). IWMM models assume that each observation has coordinates in a latent space where the data is Gaussian distributed - an IGMM is then learned in that latent space instead. We show that the IWMM model outperforms an IGMM based approach to novelty detection for hyperspectral image analysis.