Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Convex Optimization
Adaptive dimension reduction using discriminant analysis and K-means clustering
Proceedings of the 24th international conference on Machine learning
Spectral feature selection for supervised and unsupervised learning
Proceedings of the 24th international conference on Machine learning
Spectral Regression: A Unified Approach for Sparse Subspace Learning
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Trace ratio criterion for feature selection
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Multi-task feature learning via efficient l2, 1-norm minimization
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Unsupervised feature selection for multi-cluster data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Discriminative codeword selection for image representation
Proceedings of the international conference on Multimedia
A Variance Minimization Criterion to Feature Selection Using Laplacian Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Joint feature selection and subspace learning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Feature selection via joint embedding learning and sparse regression
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
l2,1-norm regularized discriminative feature selection for unsupervised learning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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Due to the absence of class labels, unsupervised feature selection is much more difficult than supervised feature selection. Traditional unsupervised feature selection algorithms usually select features to preserve the structure of the data set. Inspired from the recent developments on discriminative clustering, we propose in this paper a novel unsupervised feature selection approach via Joint Clustering and Feature Selection (JCFS). Specifically, we integrate Fisher score into the clustering framework. We select those features such that the fisher criterion is maximized and the manifold structure can be best preserved simultaneously. We also discover the connection between JCFS and other clustering and feature selection methods, such as discriminative K-means, JELSR and DCS. Experimental results on real world data sets demonstrated the effectiveness of the proposed algorithm.