  Multi-label classification (MLC) refers to the problem of tagging a given
instance with a set of relevant labels. Most existing MLC methods are based on
the assumption that the correlation of two labels in each label pair is
symmetric, which is violated in many real-world scenarios. Moreover, most
existing methods design learning processes associated with the number of
labels, which makes their computational complexity a bottleneck when scaling up
to large-scale output space. To tackle these issues, we propose a novel MLC
learning method named Scalable Label Distribution Learning (SLDL) for
multi-label classification which can describe different labels as distributions
in a latent space, where the label correlation is asymmetric and the dimension
is independent of the number of labels. Specifically, SLDL first converts
labels into continuous distributions within a low-dimensional latent space and
leverages the asymmetric metric to establish the correlation between different
labels. Then, it learns the mapping from the feature space to the latent space,
resulting in the computational complexity is no longer related to the number of
labels. Finally, SLDL leverages a nearest-neighbor-based strategy to decode the
latent representations and obtain the final predictions. Our extensive
experiments illustrate that SLDL can achieve very competitive classification
performances with little computational consumption.
