Model-based Classification and Novelty Detection For Point Pattern Data
This work addresses a domain-specific problem for researchers analyzing point pattern data, offering incremental advancements in modeling techniques.
The paper tackles the lack of statistical models for point pattern data in classification and novelty detection by proposing random finite sets (RFS) models, resulting in substantial performance improvements in novelty detection.
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.