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Multi-label classification has received considerable interest in recentyears. 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, consideringcorrelations between labels, as well as exploiting unlabeled data to improveprediction performance. To tackle datasets with a large set of labels,embedding-based methods have been proposed which seek to represent the labelassignments in a low-dimensional space. Many state-of-the-art embedding-basedmethods use a linear dimensionality reduction to represent the labelassignments in a low-dimensional space. However, by doing so, these methodsactually neglect the tail labels - labels that are infrequently assigned toinstances. We propose an embedding-based method that non-linearly embeds thelabel vectors using an stochastic approach, thereby predicting the tail labelsmore accurately. Moreover, the proposed method have excellent mechanisms forhandling missing labels, dealing with large-scale datasets, as well asexploiting unlabeled data. With the best of our knowledge, our proposed methodis the first multi-label classifier that simultaneously addresses all of thementioned challenges. Experiments on real-world datasets show that our methodoutperforms stateof-the-art multi-label classifiers by a large margin, in termsof prediction performance, as well as training time.

An Efficient Large-scale Semi-supervised Multi-label Classifier Capable of Handling Missing labels