Towards scalable Bayesian nonparametric methods for data analytics

Resorting big data to actionable information involves dealing with four dimensions of challenges in big data (called four V&rsquo;s): volume, variety, velocity, veracity. In this study, we seek for novel Bayesian nonparametric models and scalable learning algorithms which can deal with these challenges of the big data era.<br /

Model-based classification and novelty detection for point pattern data

© 2016 IEEE. Point patterns are sets or multi-sets of unordered elements that can be found in numerous data sources. However, in data analysis tasks such as classification and novelty detection, appropriate statistical models for point pattern data have not received much attention. This paper proposes the modelling of point pattern data via random finite sets (RFS). In particular, we propose appropriate likelihood functions, and a maximum likelihood estimator for learning a tractable family of RFS models. In novelty detection, we propose novel ranking functions based on RFS models, which substantially improve performance