Latent variable models and factors analysis
Latent variable models and factors analysis
Divergence Based Feature Selection for Multimodal Class Densities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Statistics & Data Analysis - Special issue on classification
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Classification of binary vectors by stochastic complexity
Journal of Multivariate Analysis
Theory of keyblock-based image retrieval
ACM Transactions on Information Systems (TOIS)
On predictive distributions and Bayesian networks
Statistics and Computing
Classification of binary vectors by using ΔSC distance to minimize stochastic complexity
Pattern Recognition Letters
MDL estimation for small sample sizes and its application to segmenting binary strings
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
A general model for clustering binary data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Review: Which is the best way to organize/classify images by content?
Image and Vision Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Block clustering with Bernoulli mixture models: Comparison of different approaches
Computational Statistics & Data Analysis
A Statistical Approach for Binary Vectors Modeling and Clustering
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
The 2005 PASCAL visual object classes challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Finding uninformative features in binary data
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
IEEE Transactions on Signal Processing
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
A robust approach for multivariate binary vectors clustering and feature selection
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Visitors of two types of museums: A segmentation study
Expert Systems with Applications: An International Journal
On online high-dimensional spherical data clustering and feature selection
Engineering Applications of Artificial Intelligence
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This paper presents an approach that partitions data sets of unlabeled binary vectors without a priori information about the number of clusters or the saliency of the features. The unsupervised binary feature selection problem is approached using finite mixture models of multivariate Bernoulli distributions. Using stochastic complexity, the proposed model determines simultaneously the number of clusters in a given data set composed of binary vectors and the saliency of the features used. We conduct different applications involving real data, document classification and images categorization to show the merits of the proposed approach.