A Two-Stage Bayesian Semiparametric Model for Novelty Detection with Robust Prior Information

TL;DR

This paper introduces a two-stage Bayesian semiparametric model for novelty detection that robustly incorporates prior information, effectively identifying new patterns in multivariate and functional data even with contaminated known classes.

Contribution

The paper presents a novel two-stage Bayesian semiparametric approach that handles contamination and extracts prior information for improved novelty detection in complex data.

Findings

Effective detection of diverse unknown patterns in simulations.

Successful application to multivariate and functional datasets.

Robust prior extraction enhances novelty separation.

Abstract

Novelty detection methods aim at partitioning the test units into already observed and previously unseen patterns. However, two significant issues arise: there may be considerable interest in identifying specific structures within the novelty, and contamination in the known classes could completely blur the actual separation between manifest and new groups. Motivated by these problems, we propose a two-stage Bayesian semiparametric novelty detector, building upon prior information robustly extracted from a set of complete learning units. We devise a general-purpose multivariate methodology that we also extend to handle functional data objects. We provide insights on the model behavior by investigating the theoretical properties of the associated semiparametric prior. From the computational point of view, we propose a suitable $\boldsymbol{\xi}$-sequence to construct an independent slice-efficient sampler that takes into account the difference between manifest and novelty components. We showcase our model performance through an extensive simulation study and applications on both multivariate and functional datasets, in which diverse and distinctive unknown patterns are discovered.