Variational learning of finite Dirichlet mixture models using component splitting

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• 作者：Wentao Fan ; Nizar Bouguila ; Djemel Ziou
• 关键词：Mixture models ; Dirichlet distribution ; Variational inference ; Component splitting ; Object detection ; Text clustering
• 刊名：Neurocomputing
• 出版年：10 April, 2014
• 年：2014
• 卷：129
• 期：Complete
• 页码：3-16
• 全文大小：2071 K

文摘

Finite Dirichlet mixture models have proved to be an effective knowledge representation and inference engine in several machine learning and data mining applications. In this paper, we address the task of learning and selecting finite Dirichlet mixture models in an incremental variational way. A learning algorithm based on component splitting and local model selection is proposed. The merits of the proposed approach are illustrated using synthetic data as well as real challenging applications involving object detection, text documents clustering and distinguishing photographic images from computer graphics.