  Multi-label classification has received considerable interest in recent
years. Multi-label classifiers have to address many problems including:
handling large-scale datasets with many instances and a large set of labels,
compensating missing label assignments in the training set, considering
correlations between labels, as well as exploiting unlabeled data to improve
prediction performance. To tackle datasets with a large set of labels,
embedding-based methods have been proposed which seek to represent the label
assignments in a low-dimensional space. Many state-of-the-art embedding-based
methods use a linear dimensionality reduction to represent the label
assignments in a low-dimensional space. However, by doing so, these methods
actually neglect the tail labels - labels that are infrequently assigned to
instances. We propose an embedding-based method that non-linearly embeds the
label vectors using an stochastic approach, thereby predicting the tail labels
more accurately. Moreover, the proposed method have excellent mechanisms for
handling missing labels, dealing with large-scale datasets, as well as
exploiting unlabeled data. With the best of our knowledge, our proposed method
is the first multi-label classifier that simultaneously addresses all of the
mentioned challenges. Experiments on real-world datasets show that our method
outperforms stateof-the-art multi-label classifiers by a large margin, in terms
of prediction performance, as well as training time.
