南京大学学报(自然科学版) ›› 2019, Vol. 55 ›› Issue (1): 92–101.doi: 10.13232/j.cnki.jnju.2019.01.009
马宏亮，万建武*，王洪元
Ma Hongliang，Wan Jianwu*，Wang Hongyuan
摘要： 现有的多标记降维算法常通过学习标记相关性构建样本间的相似关系，进而提高学习系统的性能. 然而，在实际应用中，样本的标记信息可能存在噪声，且部分标记信息可能缺失，因此由样本的标记信息学得的标记相关性可能不准确，无法有效挖掘样本间的相似关系. 为了解决该问题，从样本的特征空间与标记空间两个方面构建样本间的相似关系. 在利用标记空间学习标记相关性的同时，通过引入特征空间中的概率超图模型，提出一种嵌入样本流形结构与标记相关性的多标记降维算法. 在十个多标记数据集和六种评价准则上的实验结果证明了所提算法的有效性．
中图分类号:
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