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中文摘要：
最大均值差异嵌入（Maximum Mean Discrepancy Embedding,MMDE）作为一种基于最大均值差异（MaximumMean Discrepancy,MMD）度量的特征提取方法被成功地运用.然而通过分析得知,该方法在处理原始输入空间上的特征提取问题时一定程度上缺乏适应性.因此本文在MMD准则的基础上,并结合已经被广泛研究和探讨的局部学习方法,提出一个新的评价度量：最大局部加权均值差异（Maximum LocalWeightedMean Discrepancy,MLMD）,该度量反映源域和目标域分布差异时能充分考虑两个区域内在的局部结构,同时还能通过局部分布差异去反映全局分布差异.本文还在此度量的基础上提出一种能实现迁移学习任务并具有一定局部学习能力的特征提取方法：最大局部加权均值差异嵌入（Maximum Local WeightedMean Discrepancy Embedding,MWME）.该方法不但能完成传统意义上的特征提取,同时还能完成在两个分布存在差异但相关的两个区域上实现领域适应学习,从而表明该特征提取方法具有较好的鲁棒性和适应性.实验证明MLMD准则和MWME方法具有上述优势.
英文摘要：
MMDE,regarded as a MMD-based feature extraction method,has been successfully used.However,when the feature extraction problems of the original input space have been solved,the MMDE lacks the suitability to some extent.Therefore,we propose Maximum Local Weighted Mean Discrepancy（MLMD）by integrating the theory and technique of local learning methods.The measurement considers fully the internal local structure between domains;at the same time,the global distribution discrepancy can be reflected by the local distribution discrepancy.We also,based on the above measurement,propose Maximum Local Weighted Mean Discrepancy Embedding（MWME）,which not only fulfills transfer learning task but also has certain local learning capability.The MWME can complete traditional feature extraction as well as domain adaptation learning in two domains whose distributions are different but relative,thus indicating its better robustness and adaptation.Tests show the above-proposed advantages of the MLMD criterion and the MWME method.
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