Abstract
Hierarchical clustering is a widely used tool for structuring and visualizing complex data using similarity. Traditionally, hierarchical clustering is based on local heuristics that do not explicitly provide assessment of the statistical saliency of the extracted hierarchy. We propose a non-parametric generative model for hierarchical clustering of similarity based on multifurcating Gibbs fragmentation trees. This allows us to infer and display the posterior distribution of hierarchical structures that comply with the data. We demonstrate the utility of our method on synthetic data and data of functional brain connectivity.
Original language | English |
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Title of host publication | Proceedings of the 4th International Workshop on Cognitive Information Processing |
Number of pages | 6 |
Publisher | IEEE |
Publication date | 2014 |
DOIs | |
Publication status | Published - 2014 |
Event | 4th International Workshop on Cognitive Information Processing (CIP 2014) - Copenhagen, Denmark Duration: 26 Apr 2014 → 28 Apr 2014 http://cip2014.conwiz.dk |
Workshop
Workshop | 4th International Workshop on Cognitive Information Processing (CIP 2014) |
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Country/Territory | Denmark |
City | Copenhagen |
Period | 26/04/2014 → 28/04/2014 |
Internet address |
Keywords
- Bioengineering
- Communication, Networking and Broadcast Technologies
- Computing and Processing
- Robotics and Control Systems
- Signal Processing and Analysis