Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Fisher discriminant analysis for supervised dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Spectral feature selection for supervised and unsupervised learning
Proceedings of the 24th international conference on Machine learning
Convex multi-task feature learning
Machine Learning
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
Image clustering using local discriminant models and global integration
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Tag localization with spatial correlations and joint group sparsity
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Exploiting the entire feature space with sparsity for automatic image annotation
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Unsupervised feature selection for linked social media data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Self-taught dimensionality reduction on the high-dimensional small-sized data
Pattern Recognition
Hypergraph spectra for semi-supervised feature selection
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Hypergraph spectra for unsupervised feature selection
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Feature selection for high-dimensional imbalanced data
Neurocomputing
Local 3d symmetry for visual saliency in 2.5d point clouds
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
We are not equally negative: fine-grained labeling for multimedia event detection
Proceedings of the 21st ACM international conference on Multimedia
GLocal structural feature selection with sparsity for multimedia data understanding
Proceedings of the 21st ACM international conference on Multimedia
Joint clustering and feature selection
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Probabilistic multi-label classification with sparse feature learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Co-regularized ensemble for feature selection
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Robust unsupervised feature selection
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Supervised feature subset selection with ordinal optimization
Knowledge-Based Systems
Integration of dense subgraph finding with feature clustering for unsupervised feature selection
Pattern Recognition Letters
Feature selection with SVD entropy: Some modification and extension
Information Sciences: an International Journal
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Compared with supervised learning for feature selection, it is much more difficult to select the discriminative features in unsupervised learning due to the lack of label information. Traditional unsupervised feature selection algorithms usually select the features which best preserve the data distribution, e.g., manifold structure, of the whole feature set. Under the assumption that the class label of input data can be predicted by a linear classifier, we incorporate discriminative analysis and l2,1-norm minimization into a joint framework for unsupervised feature selection. Different from existing unsupervised feature selection algorithms, our algorithm selects the most discriminative feature subset from the whole feature set in batch mode. Extensive experiment on different data types demonstrates the effectiveness of our algorithm.