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 propos...
| Main Authors: | , , , |
|---|---|
| Format: | Conference Paper |
| Published: |
2017
|
| Online Access: | http://hdl.handle.net/20.500.11937/56163 |
| _version_ | 1848759802088390656 |
|---|---|
| author | Vo, Ba Tuong Tran, N. Phung, D. Vo, Ba-Ngu |
| author_facet | Vo, Ba Tuong Tran, N. Phung, D. Vo, Ba-Ngu |
| author_sort | Vo, Ba Tuong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 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. |
| first_indexed | 2025-11-14T10:05:40Z |
| format | Conference Paper |
| id | curtin-20.500.11937-56163 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:05:40Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-561632018-03-29T09:09:01Z Model-based classification and novelty detection for point pattern data Vo, Ba Tuong Tran, N. Phung, D. Vo, Ba-Ngu © 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. 2017 Conference Paper http://hdl.handle.net/20.500.11937/56163 10.1109/ICPR.2016.7900030 restricted |
| spellingShingle | Vo, Ba Tuong Tran, N. Phung, D. Vo, Ba-Ngu Model-based classification and novelty detection for point pattern data |
| title | Model-based classification and novelty detection for point pattern data |
| title_full | Model-based classification and novelty detection for point pattern data |
| title_fullStr | Model-based classification and novelty detection for point pattern data |
| title_full_unstemmed | Model-based classification and novelty detection for point pattern data |
| title_short | Model-based classification and novelty detection for point pattern data |
| title_sort | model-based classification and novelty detection for point pattern data |
| url | http://hdl.handle.net/20.500.11937/56163 |