基于最大均值差异的多标记迁移学习算法

针对多标记迁移学习中源领域与目标领域的特征分布差异会导致源领域数据无法被目标领域利用的问题，提出了一种基于最大均值差异的多标记迁移学习算法（Multi-Label Transfer Learning via Maximum mean discrepancy，M-MLTL），算法通过分解关系矩阵构造共享子空间，并采用最大均值差异（maximum mean discrepancy）作为评价指标，最小化子空间特征的分布差异，从而使源领域与目标领域的特征分布尽可能相似．多标记图像分类实验的结果表明，新算法比同类算法有更高的精度和计算效率．

Multi-label Transfer Learning via Maximum Mean Discrepancy

Due to the different distribution of features between the source and target domains in a multi-label transfer learning problem, source domain data cannot exert any effect. To resolve this problem, here we propose novel multi-label transfer learning via the maximum mean discrepancy. The proposed algorithm decomposes a relational matrix to learn a common subspace. Furthermore, we incorporate the empirical maximum mean discrepancy into the objective function of matrix factorization to minimize the probability distance between different domains. Experimental results from multi-label classification demonstrate that the proposed approach achieves better performance than other similar algorithms in terms of accuracy and efficiency.