期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 21, 期 12, 页码 1649-1664出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2009.42
关键词
Discrete data; finite mixture models; multinomial; Dirichlet prior; feature weighting/selection; MAP; stochastic complexity; Fisher kernel; image databases; text clustering
类别
资金
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- NATEQ (Le Fonds Quebecois de la Recherche sur la Nature et les Technologies)
- Nouveaux Chercheurs Grant
- Concordia University
In this paper, we consider the problem of unsupervised discrete feature selection/weighting. Indeed, discrete data are an important component in many data mining, machine learning, image processing, and computer vision applications. However, much of the published work on unsupervised feature selection has concentrated on continuous data. We propose a probabilistic approach that assigns relevance weights to discrete features that are considered as random variables modeled by finite discrete mixtures. The choice of finite mixture models is justified by its flexibility which has led to its widespread application in different domains. For the learning of the model, we consider both Bayesian and information-theoretic approaches through stochastic complexity. Experimental results are presented to illustrate the feasibility and merits of our approach on a difficult problem which is clustering and recognizing visual concepts in different image data. The proposed approach is successfully applied also for text clustering.
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