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Nonparametric Statistical Structuring of Knowledge Systems Using Binary Feature Matches

Research output: Chapter in Book/Report/Conference proceedingArticle in 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.

Publication information

Original languageEnglish
Title of host publicationProceedings of MLSP 2014
EditorsMamadou Mboup, Tülay Adali, Éric Moreau, Jan Larsen
Number of pages6
Place of PublicationNew York
PublisherIEEE
Publication date2014
ISBN (Print)9781479936946
DOIs
StatePublished - 2014
EventThe 24th IEEE International Workshop on Machine Learning for Signal Processing. MLSP 2014 - Reims Centre des Congrès, Reims, France
Duration: 21 Sep 201424 Sep 2014
Conference number: 24
http://mlsp2014.conwiz.dk/home.htm

Conference

ConferenceThe 24th IEEE International Workshop on Machine Learning for Signal Processing. MLSP 2014
Nummer24
LocationReims Centre des Congrès
LandFrance
ByReims
Periode21/09/201424/09/2014
Internetadresse

Bibliographical note

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    Research areas

  • Bayesian non-parametrics, Relational modeling, Binary similarity, Knowledge structuring

ID: 42559987