Large margin metric learning for multi-label prediction

Publication Type:
Conference Proceeding
Citation:
Proceedings of the National Conference on Artificial Intelligence, 2015, 4 pp. 2800 - 2806
Issue Date:
2015-06-01
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
l.pdfPublished version559.25 kB
Adobe PDF
Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Canonical correlation analysis (CCA) and maximum margin output coding (MMOC) methods have shown promising results for multi-label prediction, where each instance is associated with multiple labels. However, these methods require an expensive decoding procedure to recover the multiple labels of each testing instance. The testing complexity becomes unacceptable when there are many labels. To avoid decoding completely, we present a novel large margin metric learning paradigm for multi-label prediction. In particular, the proposed method learns a distance metric to discover label dependency such that instances with very different multiple labels will be moved far away. To handle many labels, we present an accelerated proximal gradient procedure to speed up the learning process. Comprehensive experiments demonstrate that our proposed method is significantly faster than CCA and MMOC in terms of both training and testing complexities. Moreover, our method achieves superior prediction performance compared with state-of-the-art methods.
Please use this identifier to cite or link to this item: