Abstract
Constrained clustering is an important machine learning, signal processing and data mining tool, for discovering clusters in data, in the presence of additional domain information. The present work introduces a probabilistic scheme for constrained clustering based on the popular Gaussian Process framework. The proposed scheme accommodates pairwise, must- and cannot-link constraints between data, does not require hyperparameter tuning, and enables assessment of the reliability of obtained results. Preliminary results on real data showcase the potential of the proposed approach.
| Original language | English (US) |
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| Title of host publication | 28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings |
| Publisher | European Signal Processing Conference, EUSIPCO |
| Pages | 1457-1461 |
| Number of pages | 5 |
| ISBN (Electronic) | 9789082797053 |
| DOIs | |
| State | Published - Jan 24 2021 |
| Event | 28th European Signal Processing Conference, EUSIPCO 2020 - Amsterdam, Netherlands Duration: Aug 24 2020 → Aug 28 2020 |
Publication series
| Name | European Signal Processing Conference |
|---|---|
| Volume | 2021-January |
| ISSN (Print) | 2219-5491 |
Conference
| Conference | 28th European Signal Processing Conference, EUSIPCO 2020 |
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| Country/Territory | Netherlands |
| City | Amsterdam |
| Period | 8/24/20 → 8/28/20 |
Bibliographical note
Funding Information:Work in this paper was supported by NSF grants 1500713, 1514056, 1711471, and 1901134. Emails: {traga003,georgios}@umn.edu
Publisher Copyright:
© 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.
Keywords
- Clustering
- Constrained clustering
- Gaussian process