  Partial multi-label learning (PML) models the scenario where each training
instance is annotated with a set of candidate labels, and only some of the
labels are relevant. The PML problem is practical in real-world scenarios, as
it is difficult and even impossible to obtain precisely labeled samples.
Several PML solutions have been proposed to combat with the prone misled by the
irrelevant labels concealed in the candidate labels, but they generally focus
on the smoothness assumption in feature space or low-rank assumption in label
space, while ignore the negative information between features and labels.
Specifically, if two instances have largely overlapped candidate labels,
irrespective of their feature similarity, their ground-truth labels should be
similar; while if they are dissimilar in the feature and candidate label space,
their ground-truth labels should be dissimilar with each other. To achieve a
credible predictor on PML data, we propose a novel approach called PML-LFC
(Partial Multi-label Learning with Label and Feature Collaboration). PML-LFC
estimates the confidence values of relevant labels for each instance using the
similarity from both the label and feature spaces, and trains the desired
predictor with the estimated confidence values. PML-LFC achieves the predictor
and the latent label matrix in a reciprocal reinforce manner by a unified
model, and develops an alternative optimization procedure to optimize them.
Extensive empirical study on both synthetic and real-world datasets
demonstrates the superiority of PML-LFC.
