Semi-supervised multi-label learning with incomplete labels
The problem of incomplete labels is frequently encountered in many application domains where the training labels are obtained via crowd-sourcing. The label incompleteness significantly increases the difficulty of acquiring accurate multi-label prediction models. In this paper, we propose a novel semi-supervised multi-label method that integrates low-rank label matrix recovery into the manifold regularized vector-valued prediction framework to address multi-label learning with incomplete labels. The proposed method is formulated as a convex but non-smooth joint optimization problem over the latent label matrix and the prediction model parameters. We then develop a fast proximal gradient descent with continuation algorithm to solve it for a global optimal solution. The efficacy of the proposed approach is demonstrated on multiple multi-label datasets, comparing to related methods that handle incomplete labels.
|24th International Joint Conference on Artificial Intelligence, IJCAI 2015|
|Organisation||School of Computer Science|
Zhao, F. (Feipeng), & Guo, Y. (2015). Semi-supervised multi-label learning with incomplete labels. In IJCAI International Joint Conference on Artificial Intelligence (pp. 4062–4068).