Robust extreme multi-label learning

Publication Type:
Conference Proceeding
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, 13-17-August-2016 pp. 1275 - 1284
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
p1275-xu.pdfPublished version1.98 MB
Adobe PDF
© 2016 ACM. Tail labels in the multi-label learning problem undermine the low-rank assumption. Nevertheless, this problem has rarely been investigated. In addition to using the low-rank structure to depict label correlations, this paper explores and exploits an additional sparse component to handle tail labels behaving as outliers, in order to make the classical low-rank principle in multi-label learning valid. The divideand- conquer optimization technique is employed to increase the scalability of the proposed algorithm while theoretically guaranteeing its performance. A theoretical analysis of the generalizability of the proposed algorithm suggests that it can be improved by the low-rank and sparse decomposition given tail labels. Experimental results on real-world data demonstrate the significance of investigating tail labels and the effectiveness of the proposed algorithm.
Please use this identifier to cite or link to this item: