Partial Multi-Label Learning (PML) is an emerging weakly supervised learning framework, where each instance contains a candidate label set with only some labels being ground-truth labels. Many existing PML methods recover the information of the ground-truth label set through k-Nearest Neighbor (kNN) disambiguation. However, this popular strategy might be suboptimal, as it makes disambiguation for a given instance based solely on its neighbors' features and class labels, i.e., the local structural information in the feature space, thereby missing the opportunity to explicitly and sufficiently leverage the global structural information in the feature space to facilitate disambiguation. In this paper, we propose a novel algorithm called PRAG, i.e., PaRtiAl multi-label learning by exploiting Global information, which incorporates the global factor obtained from the features of all the training instances into the kNN disambiguation process. Specifically, we learn for each instance a global factor vector, which captures the global affinity between an instance and each label across the feature space. This global factor vector is continuously updated through iterative propagation, with each iteration computing the global factor vector based on the similarity between the instance's features and a dynamically constructed label prototype for each label. The label prototype is formed by aggregating the features of all training instances weighted by their current estimated confidence for that label. Crucially, the global factor vector serves as a weighting mechanism during aggregation of the neighbor labels in the kNN disambiguation step. It effectively injects global structural information into the local disambiguation process, providing a more robust estimation of label confidence by mitigating the limitations of relying solely on potentially noisy local neighbors. Based on the estimated label confidence, PRAG then exploits label correlations to classify instances. We conducted extensive experiments on various real and synthetic datasets, and the results show the superiority of PRAG compared to the state-of-the-art methods.

As an emerging weakly supervised learning framework, partial label learning aims to induce a multi-class classifier from ambiguous supervision information where each training example is associated with a set of candidate labels, among which only one is the true label. Traditional feature selection methods, either for single label and multiple label problems, are not applicable to partial label learning as the ambiguous information contained in the label space obfuscates the importance of features and misleads the selection process. This makes the selection of a proper feature subset from partial label examples particularly challenging, and therefore has rarely been investigated. In this paper, we propose a novel feature selection algorithm for partial label learning, named PLFS, which considers not only the relationships between features and labels, but also exploits the relationships between instances to select the most informative and important features to enhance the performance of partial label learning. PLFS constructs an adaptive weighted graph to exploit the similarity information among instances, differentiate the label space and weight the feature space, which leads to the selection of a proper feature subset. Extensive experiments over a broad range of benchmark data sets clearly validate the effectiveness of our proposed feature selection approach.

In multi-label learning, each instance is associated with multiple labels simultaneously. Multi-label data often have noisy, irrelevant, and redundant features of high dimensionality. Multi-label feature selection has received considerable attention as an effective means for dealing with high-dimensional multi-label data. Many multi-label feature selection methods exploit label correlations to help select features. However, finding label correlations and selecting features in existing multi-label feature selection methods are often two separate processes, the existence of noises and outliers in training data makes the label correlations exploited from label space less reliable. Therefore, the learned label correlations may mislead the feature selection process and result in the selection of less informative features. This article proposes a novel algorithm named ROAD, i.e., multi-label featuRe selectiOn via ADaptive label correlation estimation. ROAD jointly performs adaptive label correlation exploration and feature selection with alternating optimization to obtain reliable estimation of label correlations, which can more effectively reveal the intrinsic manifold structure among labels and lead to the selection of a more proper feature subset. Comprehensive experiments on several frequently used datasets validate the superiority of ROAD against the state-of-the-art multi-label feature selection algorithms.