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