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After being trained on a fully-labeled training set, where the observationsare grouped into a certain number of known classes, novelty detection methodsaim to classify the instances of an unlabeled test set while allowing for thepresence of previously unseen classes. These models are valuable in many areas,ranging from social network and food adulteration analyses to biology, where anevolving population may be present. In this paper, we focus on a two-stageBayesian semiparametric novelty detector, also known as Brand, recentlyintroduced in the literature. Leveraging on a model-based mixturerepresentation, Brand allows clustering the test observations into knowntraining terms or a single novelty term. Furthermore, the novelty term ismodeled with a Dirichlet Process mixture model to flexibly capture anydeparture from the known patterns. Brand was originally estimated using MCMCschemes, which are prohibitively costly when applied to high-dimensional data.To scale up Brand applicability to large datasets, we propose to resort to avariational Bayes approach, providing an efficient algorithm for posteriorapproximation. We demonstrate a significant gain in efficiency and excellentclassification performance with thorough simulation studies. Finally, toshowcase its applicability, we perform a novelty detection analysis using theopenly-available Statlog dataset, a large collection of satellite imagingspectra, to search for novel soil types.

Variational Inference for Semiparametric Bayesian Novelty Detection in Large Datasets