Automatic model selection in archetype analysis

Prabhakaran, Sandhya and Raman, Sudhir and Vogt, Julia and Roth, Volker. (2012) Automatic model selection in archetype analysis. In: Pattern recognition : joint 34th DAGM and 36th OAGM Symposium. Springer, pp. 458-467.

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Official URL: http://edoc.unibas.ch/dok/A6018451

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Archetype analysis involves the identification of representative objects from amongst a set of multivariate data such that the data can be expressed as a convex combination of these representative objects. Existing methods for archetype analysis assume a fixed number of archetypes a priori. Multiple runs of these methods for different choices of archetypes are required for model selection. Not only is this computationally infeasible for larger datasets, in heavy-noise settings model selection becomes cumbersome. In this paper, we present a novel extension to these existing methods with the specific focus of relaxing the need to provide a fixed number of archetypes beforehand. Our fast iterative optimization algorithm is devised to automatically select the right model using BIC scores and can easily be scaled to noisy, large datasets. These benefits are achieved by introducing a Group-Lasso component popular for sparse linear regression. The usefulness of the approach is demonstrated through simulations and on a real world application of document analysis for identifying topics.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Biomedical Data Analysis (Roth)
UniBasel Contributors:Roth, Volker and Vogt, Julia and Prabhakaran, Sandhya and Shankar Raman, Sudhir
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Series Name:Lecture notes in computer science
Issue Number:7476
Note:Publication type according to Uni Basel Research Database: Conference paper
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Last Modified:07 Mar 2014 10:01
Deposited On:13 Sep 2013 07:58

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