Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Visualization and interactive feature selection for unsupervised data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Feature Selection for Clustering - A Filter Solution
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
Efficiently handling feature redundancy in high-dimensional data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
IEEE Transactions on Knowledge and Data Engineering
Bayesian Feature and Model Selection for Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantic principal video shot classification via mixture Gaussian
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Multimedia
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Which Components are Important for Interactive Image Searching?
IEEE Transactions on Circuits and Systems for Video Technology
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Engineering Applications of Artificial Intelligence
A unifying criterion for unsupervised clustering and feature selection
Pattern Recognition
An unsupervised feature selection framework based on clustering
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Model-based clustering of high-dimensional data: Variable selection versus facet determination
International Journal of Approximate Reasoning
A new hybrid intelligent system for accurate detection of Parkinson's disease
Computer Methods and Programs in Biomedicine
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With the wide applications of Gaussian mixture clustering, e.g., in semantic video classification [H. Luo, J. Fan, J. Xiao, X. Zhu, Semantic principal video shot classification via mixture Gaussian, in: Proceedings of the 2003 International Conference on Multimedia and Expo, vol. 2, 2003, pp. 189-192], it is a nontrivial task to select the useful features in Gaussian mixture clustering without class labels. This paper, therefore, proposes a new feature selection method, through which not only the most relevant features are identified, but the redundant features are also eliminated so that the smallest relevant feature subset can be found. We integrate this method with our recently proposed Gaussian mixture clustering approach, namely rival penalized expectation-maximization (RPEM) algorithm [Y.M. Cheung, A rival penalized EM algorithm towards maximizing weighted likelihood for density mixture clustering with automatic model selection, in: Proceedings of the 17th International Conference on Pattern Recognition, 2004, pp. 633-636; Y.M. Cheung, Maximum weighted likelihood via rival penalized EM for density mixture clustering with automatic model selection, IEEE Trans. Knowl. Data Eng. 17(6) (2005) 750-761], which is able to determine the number of components (i.e., the model order selection) in a Gaussian mixture automatically. Subsequently, the data clustering, model selection, and the feature selection are all performed in a single learning process. Experimental results have shown the efficacy of the proposed approach.