کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
532184 869918 2013 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Variational learning of a Dirichlet process of generalized Dirichlet distributions for simultaneous clustering and feature selection
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Variational learning of a Dirichlet process of generalized Dirichlet distributions for simultaneous clustering and feature selection
چکیده انگلیسی


• An infinite generalized Dirichlet mixture for simultaneous clustering and feature selection is proposed.
• A variational approach is developed to learn the proposed model.
• The proposed statistical framework is validated by artificial data as well as two real challenging applications.

This paper introduces a novel enhancement for unsupervised feature selection based on generalized Dirichlet (GD) mixture models. Our proposal is based on the extension of the finite mixture model previously developed in [1] to the infinite case, via the consideration of Dirichlet process mixtures, which can be viewed actually as a purely nonparametric model since the number of mixture components can increase as data are introduced. The infinite assumption is used to avoid problems related to model selection (i.e. determination of the number of clusters) and allows simultaneous separation of data in to similar clusters and selection of relevant features. Our resulting model is learned within a principled variational Bayesian framework that we have developed. The experimental results reported for both synthetic data and real-world challenging applications involving image categorization, automatic semantic annotation and retrieval show the ability of our approach to provide accurate models by distinguishing between relevant and irrelevant features without over- or under-fitting the data.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 46, Issue 10, October 2013, Pages 2754–2769
نویسندگان
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