Semi-supervised multi-label classification: A simultaneous large-margin, subspace learning approach

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