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Partial multi-label learning (PML) models the scenario where each traininginstance is annotated with a set of candidate labels, and only some of thelabels are relevant. The PML problem is practical in real-world scenarios, asit is difficult and even impossible to obtain precisely labeled samples.Several PML solutions have been proposed to combat with the prone misled by theirrelevant labels concealed in the candidate labels, but they generally focuson the smoothness assumption in feature space or low-rank assumption in labelspace, 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 besimilar; while if they are dissimilar in the feature and candidate label space,their ground-truth labels should be dissimilar with each other. To achieve acredible predictor on PML data, we propose a novel approach called PML-LFC(Partial Multi-label Learning with Label and Feature Collaboration). PML-LFCestimates the confidence values of relevant labels for each instance using thesimilarity from both the label and feature spaces, and trains the desiredpredictor with the estimated confidence values. PML-LFC achieves the predictorand the latent label matrix in a reciprocal reinforce manner by a unifiedmodel, and develops an alternative optimization procedure to optimize them.Extensive empirical study on both synthetic and real-world datasetsdemonstrates the superiority of PML-LFC.