Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Floating search methods in feature selection
Pattern Recognition Letters
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature subset selection using a new definition of classifiability
Pattern Recognition Letters
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Linear and Non-linear Geometric Object Matching with Implicit Representation
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Adaptive mixtures of local experts
Neural Computation
Robust subspace clustering by combined use of kNND metric and SVD algorithm
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An on-line signature verification system based on fusion of local and global information
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Expert Systems with Applications: An International Journal
Input Decimated Ensemble based on Neighborhood Preserving Embedding for spectrogram classification
Expert Systems with Applications: An International Journal
Taking advantage of class-specific feature selection
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Selecting features from multiple feature sets for SVM committee-based screening of human larynx
Expert Systems with Applications: An International Journal
Reduced Reward-punishment editing for building ensembles of classifiers
Expert Systems with Applications: An International Journal
General framework for class-specific feature selection
Expert Systems with Applications: An International Journal
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The study of feature selection methods has become an area of intensive research in pattern recognition. In this paper, a new feature selection approach, called cluster-based pattern discrimination (CPD), is introduced. Classes are independently partitioned into clusters to group together similar patterns: a different subspace is defined for each cluster by determining an optimal subset of features. The similarity between an unknown pattern x and a given cluster is computed through a classifier. To combine these similarities we use the ''max rule'' which simply assigns each pattern to the class that contains the cluster for which the pattern has the maximum similarity. Moreover, extensive experiments carried out on different databases prove the advantages of the proposed approach.