Implicitly Restarted Arnoldi Methods and Subspace Iteration
SIAM Journal on Matrix Analysis and Applications
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
The Journal of Machine Learning Research
Unsupervised feature selection for principal components analysis
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying biologically relevant genes via multiple heterogeneous data sources
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Heterogeneous data fusion for alzheimer's disease study
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Structure feature selection for graph classification
Proceedings of the 17th ACM conference on Information and knowledge management
Feature Selection for Clustering on High Dimensional Data
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
An improved approximation algorithm for the column subset selection problem
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Feature selection with dynamic mutual information
Pattern Recognition
An Iterative Hybrid Filter-Wrapper Approach to Feature Selection for Document Clustering
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
A General Framework of Feature Selection for Text Categorization
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Graph-Based Discrete Differential Geometry for Critical Instance Filtering
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Variational Graph Embedding for Globally and Locally Consistent Feature Extraction
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Trace ratio criterion for feature selection
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Forward semi-supervised feature selection
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Feature selection for Bayesian network classifiers using the MDL-FS score
International Journal of Approximate Reasoning
Unsupervised feature selection for multi-cluster data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Intelligent negotiation behaviour model for an open railway access market
Expert Systems with Applications: An International Journal
Discriminative semi-supervised feature selection via manifold regularization
IEEE Transactions on Neural Networks
BASSUM: A Bayesian semi-supervised method for classification feature selection
Pattern Recognition
Constraint scores for semi-supervised feature selection: A comparative study
Pattern Recognition Letters
Hybrid feature selection method for supervised classification based on Laplacian score ranking
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Constrained laplacian score for semi-supervised feature selection
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Eigenvector sensitive feature selection for spectral clustering
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Feature subset selection with cumulate conditional mutual information minimization
Expert Systems with Applications: An International Journal
A New Unsupervised Feature Ranking Method for Gene Expression Data Based on Consensus Affinity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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
Unsupervised feature selection for linked social media data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient feature selection filters for high-dimensional data
Pattern Recognition Letters
Feature extraction in protein sequences classification: a new stability measure
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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
Massively parallel feature selection: an approach based on variance preservation
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Automatic dimensionality estimation for manifold learning through optimal feature selection
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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
Unsupervised Feature Selection with Feature Clustering
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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
G-Optimal Feature Selection with Laplacian regularization
Neurocomputing
Joint clustering and feature selection
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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
Quality of information-based source assessment and selection
Neurocomputing
Feature selection for k-means clustering stability: theoretical analysis and an algorithm
Data Mining and Knowledge Discovery
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Feature selection aims to reduce dimensionality for building comprehensible learning models with good generalization performance. Feature selection algorithms are largely studied separately according to the type of learning: supervised or unsupervised. This work exploits intrinsic properties underlying supervised and unsupervised feature selection algorithms, and proposes a unified framework for feature selection based on spectral graph theory. The proposed framework is able to generate families of algorithms for both supervised and unsupervised feature selection. And we show that existing powerful algorithms such as ReliefF (supervised) and Laplacian Score (unsupervised) are special cases of the proposed framework. To the best of our knowledge, this work is the first attempt to unify supervised and unsupervised feature selection, and enable their joint study under a general framework. Experiments demonstrated the efficacy of the novel algorithms derived from the framework.