  We propose to formulate multi-label learning as a estimation of class
distribution in a non-linear embedding space, where for each label, its
positive data embeddings and negative data embeddings distribute compactly to
form a positive component and negative component respectively, while the
positive component and negative component are pushed away from each other. Duo
to the shared embedding space for all labels, the distribution of embeddings
preserves instances' label membership and feature matrix, thus encodes the
feature-label relation and nonlinear label dependency. Labels of a given
instance are inferred in the embedding space by measuring the probabilities of
its belongingness to the positive or negative components of each label.
Specially, the probabilities are modeled as the distance from the given
instance to representative positive or negative prototypes. Extensive
experiments validate that the proposed solution can provide distinctively more
accurate multi-label classification than other state-of-the-art algorithms.
