Label-Specific Feature Learning for Multi-Label Classification: A Survey

In multi-label classification, each instance can be associated with multiple class labels simultaneously. However, each label is supposed to possess specific characteristics of its own and thus naturally shows distinct discriminative preferences on features. With consideration on this property, label-specific feature learning has emerged as a promising strategy for multi-label classification, which constructs features specific to each label to facilitate multi-label discrimination process. This paper aims to provide a timely review on this emerging modeling strategy, focusing on main progress made during the last decade. Firstly, fundamentals on label-specific feature learning including formal definition and key challenges are provided. Then, six representative label-specific feature learning algorithms are scrutinized under a concise taxonomy with necessary discussions on algorithmic properties. Lastly, open research problems in label-specific feature learning are summarized to provide possible directions for future studies.