- Authors:
-
-
Schmidt, Mikkel Nørgaard
;
Close
0000-0001-6927-8869
Department of Applied Mathematics and Computer Science, Technical University of Denmark - Herlau, Tue ;
-
Mørup, Morten
Close0000-0003-4985-4368
Department of Applied Mathematics and Computer Science, Technical University of Denmark
-
Schmidt, Mikkel Nørgaard
;
- DOI:
- 10.1109/CIP.2014.6844498
- 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.
- Type:
- Conference paper
- Language:
- English
- Published in:
- Proceedings of the 4th International Workshop on Cognitive Information Processing, 2014
- Keywords:
- Bioengineering; Communication, Networking and Broadcast Technologies; Computing and Processing; Robotics and Control Systems; Signal Processing and Analysis
- Main Research Area:
- Science/technology
- Conference:
- 4th International Workshop on Cognitive Information Processing (CIP 2014)
- Publisher:
- IEEE
- Submission year:
- 2014
- ID:
- 2282174276
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