BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Multi-label informed latent semantic indexing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Multi-labelled classification using maximum entropy method
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Efficient Learning of Label Ranking by Soft Projections onto Polyhedra
The Journal of Machine Learning Research
Model-shared subspace boosting for multi-label classification
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Closed-form supervised dimensionality reduction with generalized linear models
Proceedings of the 25th international conference on Machine learning
Multilabel classification via calibrated label ranking
Machine Learning
Convex multi-task feature learning
Machine Learning
Large scale multi-label classification via metalabeler
Proceedings of the 18th international conference on World wide web
Semi-supervised multi-label learning by constrained non-negative matrix factorization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Multi-label dimensionality reduction via dependence maximization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Exponential family sparse coding with applications to self-taught learning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A shared-subspace learning framework for multi-label classification
ACM Transactions on Knowledge Discovery from Data (TKDD)
Multi-label Feature Selection for Graph Classification
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Matrix $p$-Norms Are NP-Hard to Approximate If $p\neq1,2,\infty$
SIAM Journal on Matrix Analysis and Applications
Pattern Recognition Letters
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Labeled data is often sparse in common learning scenarios, either because it is too time consuming or too expensive to obtain, while unlabeled data is almost always plentiful. This asymmetry is exacerbated in multi-label learning, where the labeling process is more complex than in the single label case. Although it is important to consider semi-supervised methods for multi-label learning, as it is in other learning scenarios, surprisingly, few proposals have been investigated for this particular problem. In this paper, we present a new semi-supervised multi-label learning method that combines large-margin multi-label classification with unsupervised subspace learning. We propose an algorithm that learns a subspace representation of the labeled and unlabeled inputs, while simultaneously training a supervised large-margin multi-label classifier on the labeled portion. Although joint training of these two interacting components might appear intractable, we exploit recent developments in induced matrix norm optimization to show that these two problems can be solved jointly, globally and efficiently. In particular, we develop an efficient training procedure based on subgradient search and a simple coordinate descent strategy. An experimental evaluation demonstrates that semi-supervised subspace learning can improve the performance of corresponding supervised multi-label learning methods.