Robust Multi-Label Learning with Corrupted Features and Incomplete Labels
Ping Ye, Songhe Feng, Hua Yu Feng, Guojun Dai · 2019
Weakly-supervised multi-label learning has attracted wide attention recently. Most existing methods deal with such problem with incomplete labels and the feature information is ideal. However, in many scenarios, the acquired features may be corrupted due to the influence of occlusion, illumination and low-resolution, and the robustness of learning methods may be reduced. To overcome the above shortcoming, we propose a novel weakly-supervised multi-label learning algorithm, where a linear self-recovery model is adopted to reconstruct observed label information. Specifically, we first decompose the acquired feature matrix into an ideal feature matrix and an outlier matrix. To adequately utilize the visual information among instances, we introduce the graph Laplacian regularization. In addition, a linear self-recovery model is adopted to reconstruct the observed label matrix. Finally, the desired model is trained on ideal feature matrix and refined label matrix. Extensive experimental results prove the robustness of the proposed framework.