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

no code implementations • 18 Jun 2016 • Amirhossein Akbarnejad, Mahdieh Soleymani Baghshah

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.

Dimensionality Reduction Missing Labels