Model-based Classification and Novelty Detection For Point Pattern Data
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.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral ClassificationNovelty DetectionSimilar Papers 제목 키워드 기반
Model-Based Multiple Instance Learning
While Multiple Instance (MI) data are point patterns -- sets or multi-sets of unordered points -- appropriate statistical point pattern models have not been used in MI learning. This article proposes a framework for mode…
ClusteringDecision MakingGeneral Classificationmodel+2Word-level Human Interpretable Scoring Mechanism for Novel Text Detection Using Tsetlin Machines
Recent research in novelty detection focuses mainly on document-level classification, employing deep neural networks (DNN). However, the black-box nature of DNNs makes it difficult to extract an exact explanation of why …
Novelty DetectionText DetectionUtilizing Patch-level Category Activation Patterns for Multiple Class Novelty Detection
For any recognition system, the ability to identify novel class samples during inference is an important aspect of the system’s robustness. This problem of detecting novel class samples during inference is commonly refer…
Novelty DetectionNovelty Detection Meets Collider Physics
Novelty detection is the machine learning task to recognize data, which belong to an unknown pattern. Complementary to supervised learning, it allows to analyze data model-independently. We demonstrate the potential role…
ClusteringNovelty DetectionPoint Cloud Novelty Detection Based on Latent Representations of a General Feature Extractor
We propose an effective unsupervised 3D point cloud novelty detection approach, leveraging a general point cloud feature extractor and a one-class classifier. The general feature extractor consists of a graph-based autoe…
Novelty DetectionOne-Class ClassificationOne-class classifier