Spectra of regular graphs and hypergraphs and orthogonal polynomials
European Journal of Combinatorics
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Comparing Images under Variable Illumination
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Higher order learning with graphs
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
Bayesian Inference and Optimal Design for the Sparse Linear Model
The Journal of Machine Learning Research
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
Tensor Decompositions and Applications
SIAM Review
A general graph-based semi-supervised learning with novel class discovery
Neural Computing and Applications
Unsupervised feature selection for multi-cluster data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Feature Selection for Gender Classification
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Multi-way clustering using super-symmetric non-negative tensor factorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
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In many data analysis tasks, one is often confronted with the problem of selecting features from very high dimensional data. Most existing feature selection methods focus on ranking individual features based on a utility criterion, and select the optimal feature set in a greedy manner. However, the feature combinations found in this way do not give optimal classification performance, since they neglect the correlations among features. While the labeled data required by supervised feature selection can be scarce, there is usually no shortage of unlabeled data. In this paper, we propose a novel hypergraph based semi-supervised feature selection algorithm to select relevant features using both labeled and unlabeled data. There are two main contributions in this paper. The first is that by incorporating multidimensional interaction information (MII) for higher order similarities measure, we establish a novel hypergraph framework which is used for characterizing the multiple relationships within a set of samples. Thus, the structural information latent in the data can be more effectively modeled. Secondly, we derive a hypergraph subspace learning view of feature selection which casting the feature discriminant analysis into a regression framework that considers the correlations among features. As a result, we can evaluate joint feature combinations, rather than being confined to consider them individually. Experimental results demonstrate the effectiveness of our feature selection method on a number of standard face data-sets.