Abstract
In partial multi-label learning (PML), each instance
is associated with a candidate label set that contains multiple relevant labels and other false positive labels. The most challenging issue for the PML problem is that the training procedure is prone
to be affected by the labeling noise. We observe
that state-of-the-art PML methods are either powerless to disambiguate the correct labels from the
candidate labels or incapable of extracting the label correlations sufficiently. To fill this gap, a twostage DiscRiminative and correlAtive partial Multilabel leArning (DRAMA) algorithm is presented in
this work. In the first stage, a confidence value is
learned for each label by utilizing the feature manifold, which indicates how likely a label is correct.
In the second stage, a gradient boosting model is
induced to fit the label confidences. Specifically, to
explore the label correlations, we augment the feature space by the previously elicited labels on each
boosting round. Extensive experiments on various
real-world datasets clearly validate the superiority
of our proposed method