# Model-based Classification and Novelty Detection For Point Pattern Data

## TL;DR

This paper introduces a new approach for classifying and detecting novelties in point pattern data using random finite set models, with novel likelihood functions and ranking methods that enhance performance.

## Contribution

It presents a novel RFS-based modeling framework for point pattern data, including likelihood functions, estimators, and ranking methods for improved classification and novelty detection.

## Key findings

- Enhanced novelty detection performance with RFS-based ranking functions
- Development of tractable RFS models for point pattern data
- Effective maximum likelihood estimators for RFS models

## Abstract

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.