Variational learning of finite Dirichlet mixture models using component splitting

被引：15

作者：

Fan, Wentao ^{[1
]}

Bouguila, Nizar ^{[2
]}

Ziou, Djemel ^{[3
]}

机构：

[1] Concordia Univ, Dept Elect & Comp Engn, Montreal, PQ H3G 1T7, Canada

[2] Concordia Univ, CIISE, Montreal, PQ H3G 1T7, Canada

[3] Univ Sherbrooke, Dept Informat, Sherbrooke, PQ J1K 2R1, Canada

来源：

NEUROCOMPUTING | 2014年 / 129卷

基金：

加拿大自然科学与工程研究理事会;

关键词：

Mixture models; Dirichlet distribution; Variational inference; Component splitting; Object detection; Text clustering; SCENE CLASSIFICATION; BAYESIAN-ANALYSIS; RECOGNITION; ALGORITHM; SELECTION; FEATURES;

D O I：

10.1016/j.neucom.2013.03.049

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：3 / 16

页数：14

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