An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
High-performing feature selection for text classification
Proceedings of the eleventh international conference on Information and knowledge management
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Convex Optimization
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Multi-label informed latent semantic indexing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
Training SVM with indefinite kernels
Proceedings of the 25th international conference on Machine learning
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Extracting shared subspace for multi-label classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Hypergraph spectral learning for multi-label classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Cutting-plane training of structural SVMs
Machine Learning
Multi-label dimensionality reduction via dependence maximization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Subspace maximum margin clustering
Proceedings of the 18th ACM conference on Information and knowledge management
A Minimax Theorem with Applications to Machine Learning, Signal Processing, and Finance
SIAM Journal on Optimization
On multiple kernel learning with multiple labels
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Multi-label learning by exploiting label dependency
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Transfer metric learning by learning task relationships
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-label linear discriminant analysis
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Feature selection for multi-label classification using multivariate mutual information
Pattern Recognition Letters
SBIA'12 Proceedings of the 21st Brazilian conference on Advances in Artificial Intelligence
A Comparison of Multi-label Feature Selection Methods using the Problem Transformation Approach
Electronic Notes in Theoretical Computer Science (ENTCS)
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Multi-label learning studies the problem where each instance is associated with a set of labels. There are two challenges in multi-label learning: (1) the labels are interdependent and correlated, and (2) the data are of high dimensionality. In this paper, we aim to tackle these challenges in one shot. In particular, we propose to learn the label correlation and do feature selection simultaneously. We introduce a matrix-variate Normal prior distribution on the weight vectors of the classifier to model the label correlation. Our goal is to find a subset of features, based on which the label correlation regularized loss of label ranking is minimized. The resulting multi-label feature selection problem is a mixed integer programming, which is reformulated as quadratically constrained linear programming (QCLP). It can be solved by cutting plane algorithm, in each iteration of which a minimax optimization problem is solved by dual coordinate descent and projected sub-gradient descent alternatively. Experiments on benchmark data sets illustrate that the proposed methods outperform single-label feature selection method and many other state-of-the-art multi-label learning methods.