Nonparametric Statistical Structuring of Knowledge Systems Using Binary Feature Matches

Morten Mørup, Fumiko Kano Glückstad, Tue Herlau, Mikkel Nørgaard Schmidt

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review


    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
    UdgivelsesstedNew York
    ISBN (Trykt)9781479936946
    StatusUdgivet - 2014
    BegivenhedThe 24th IEEE International Workshop on Machine Learning for Signal Processing. MLSP 2014 - Reims Centre des Congrès, Reims, Frankrig
    Varighed: 21 sep. 201424 sep. 2014
    Konferencens nummer: 24


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