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 article 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.

Label-specific features work as an effective supervised feature manipulation strategy to account for distinct discriminative properties of each class label in multi-label classification. Existing approaches implement this strategy in its primal form, i.e., finding the most pertinent features specific to each class label and directly inducing classifiers on these features. Instead of such a straightforward implementation, a dual perspective for label-specific feature learning is investigated in this article. As a dual problem of existing primal one, we consider label-specific discriminative properties by identifying non-informative features for each class label and making the discrimination process immutable to variations of identified features. Accordingly, a perturbation-based approach Dela is presented, which endows classifiers with immutability on simultaneously identified non-informative features by solving a probabilistically relaxed expected risk minimization problem. Furthermore, we touch the realistic issue of label-specific feature learning in a weakly supervised scenario via extending Dela to accommodate to multi-label data with missing labels. Comprehensive experiments show that our approach outperforms the state-of-the-art counterparts.

Partial label learning induces a multi-class classifier from training examples each associated with a candidate label set where the ground-truth label is concealed. Feature selection improves the generalization ability of learning system via selecting essential features for classification from the original feature set, while the task of partial label feature selection is challenging due to ambiguous labeling information. In this paper, the first attempt towards partial label feature selection is investigated via mutual-information-based dependency maximization. Specifically, the proposed approach SAUTE iteratively maximizes the dependency between selected features and labeling information, where the value of mutual information is estimated from confidence-based latent variable inference. In each iteration, the near-optimal features are selected greedily according to properties of submodular mutual information function, while the density of latent label variable is inferred with the help of updated labeling confidences over candidate labels by resorting to kNN aggregation in the induced lower-dimensional feature space. Extensive experiments over synthetic as well as real-world partial label data sets show that the generalization ability of well-established partial label learning algorithms can be significantly improved after coupling with the proposed feature selection approach.

Partial label learning deals with training examples each associated with a set of candidate labels, among which only one is valid. Most existing works focus on manipulating the label space by estimating the labeling confidences of candidate labels, while the task of manipulating the feature space by dimensionality reduction has been rarely investigated. In this paper, a novel partial label dimensionality reduction approach named CENDA is proposed via confidence-based dependence maximization. Specifically, CENDA adapts the Hilbert-Schmidt Independence Criterion (HSIC) to help identify the projection matrix, where the dependence between projected feature information and confidence-based labeling information is maximized iteratively. In each iteration, the projection matrix admits closed-form solution by solving a tailored generalized eigenvalue problem, while the labeling confidences of candidate labels are updated by conducting kNN aggregation in the projected feature space. Extensive experiments over a broad range of benchmark data sets show that the predictive performance of well-established partial label learning algorithms can be significantly improved by coupling with the proposed dimensionality reduction approach.