Nonparametric Statistical Structuring of Knowledge Systems Using Binary Feature Matches

Publikation: Forskning - peer reviewKonferencebidrag i proceedings


Structuring knowledge systems with binary features is often based on imposing a similarity measure and clustering objects according to this similarity. Unfortunately, such analyses can be heavily influenced by the choice of similarity measure. Furthermore, it is unclear at which level clusters have statistical support and how this approach generalizes to the structuring and alignment of knowledge systems. We propose a non-parametric Bayesian generative model for structuring binary feature data that does not depend on a specific choice of similarity measure. We jointly model all combinations of binary matches and structure the data into groups at the level in which they have statistical support. The model naturally extends to structuring and aligning an arbitrary number of systems. We analyze three datasets on educational concepts and their features and demonstrate how the proposed model can both be used to structure each system separately or to jointly align two or more systems. The proposed method forms a promising new framework for the statistical modeling and alignment of structure across an arbitrary number of systems.


TitelProceedings of MLSP 2014
RedaktørerMamadou Mboup, Tülay Adali, Éric Moreau, Jan Larsen
Antal sider6
Udgivelses stedNew York
ISBN (trykt)9781479936946
StatusUdgivet - 2014
BegivenhedThe 24th IEEE International Workshop on Machine Learning for Signal Processing. MLSP 2014 - Reims, Frankrig


KonferenceThe 24th IEEE International Workshop on Machine Learning for Signal Processing. MLSP 2014
LokationReims Centre des Congrès

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