Multi-label informed latent semantic indexing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Supervised feature selection via dependence estimation
Proceedings of the 24th international conference on Machine learning
Measuring statistical dependence with hilbert-schmidt norms
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Linear dimensionality reduction for multi-label classification
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Supervised feature extraction using Hilbert-Schmidt norms
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Multilabel dimensionality reduction via dependence maximization
ACM Transactions on Knowledge Discovery from Data (TKDD)
Multi-label boosting for image annotation by structural grouping sparsity
Proceedings of the international conference on Multimedia
Multi-label feature transform for image classifications
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Multi-label linear discriminant analysis
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Image annotation by sparse logistic regression
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Gait-based human age estimation
IEEE Transactions on Information Forensics and Security
Transfer latent variable model based on divergence analysis
Pattern Recognition
Correlated multi-label feature selection
Proceedings of the 20th ACM international conference on Information and knowledge management
Labelset anchored subspace ensemble (LASE) for multi-label annotation
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Multi-label ensemble based on variable pairwise constraint projection
Information Sciences: an International Journal
Semi-supervised multi-label classification: a simultaneous large-margin, subspace learning approach
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Probabilistic multi-label classification with sparse feature learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Multi-label learning deals with data associated with multiple labels simultaneously. Like other machine learning and data mining tasks, multi-label learning also suffers from the curse of dimensionality. Although dimensionality reduction has been studied for many years, multi-label dimensionality reduction remains almost untouched. In this paper, we propose a multi-label dimensionality reduction method, MDDM, which attempts to project the original data into a lower-dimensional feature space maximizing the dependence between the original feature description and the associated class labels. Based on the Hilbert-Schmidt Independence Criterion, we derive a closed-form solution which enables the dimensionality reduction process to be efficient. Experiments validate the performance of MDDM.