Semi-Supervised Low-Rank Mapping Learning for Multi-Label Classification

Liping Jing, Liu Yang, Jian Yu, Michael K. Ng; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1483-1491


Multi-label problems arise in various domains including automatic multimedia data categorization, and have generated significant interest in computer vision and machine learning community. However, existing methods do not adequately address two key challenges: exploiting correlations between labels and making up for the lack of labeled data or even missing labels. In this paper, we proposed a semi-supervised low-rank mapping (SLRM) model to handle these two challenges. SLRM model takes advantage of the nuclear norm regularization on mapping to effectively capture the label correlations. Meanwhile, it introduces manifold regularizer on mapping to capture the intrinsic structure among data, which provides a good way to reduce the required labeled data with improving the classification performance. Furthermore, we designed an efficient algorithm to solve SLRM model based on alternating direction method of multipliers and thus it can efficiently deal with large-scale data sets. Experiments on four real-world multimedia data sets demonstrate that the proposed method can exploit the label correlations and obtain promising and better label prediction results than state-of-the-art methods.

Related Material

author = {Jing, Liping and Yang, Liu and Yu, Jian and Ng, Michael K.},
title = {Semi-Supervised Low-Rank Mapping Learning for Multi-Label Classification},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}