局部协同视角下的鲁棒标记分布学习

已有标记分布学习(LDL)算法在一定程度上破坏不同标记间的关联性及标记分布的整体结构,同时,大多仅以提升标记分布的预测性能为目的,忽略计算代价和噪声鲁棒性在实际应用中的重要性.为了缓解上述不足,文中提出基于局部协同表达的标记分布学习算法(LCR-LDL).在LCR-LDL中,一个未标记样本可被视作由该未标记样本邻域构建...

Robust Label Distribution Learning from a Perspective of Local Collaboration

In the most of label distribution learning(LDL)algorithms,the correlations among different labels and the overall structure of label distribution are destroyed to a certain extent.Moreover,most existing LDL algorithms mainly focus on improving the predictive performance of label distribution,while ignoring the significance of computational cost and noise robustness in practical applications.To tackle these issues,a local collaborative representation based label distribution learning algorithm(LCR-LDL)is proposed.In LCR-LDL,an unlabeled sample is treated as a collaborative representation of the local dictionary constructed by the neighborhood of the unlabeled sample,and the discriminating information of representation coefficients is utilized to reconstruct the label distribution of unlabeled sample.Experimental results on 15 real-world LDL datasets show that LCR-LDL effectively improves the predictive performance for LDL tasks with a better robustness and low computational cost.