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一种基于局部加权均值的领域适应学习框架

DOI: 10.3724/SP.J.1004.2013.01037, PP. 1037-1052

Keywords: 迁移学习,领域适应学习,局部加权均值,投影最大局部加权均值差异,基于局部加权均值的领域适应学习框架

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Abstract:

?最大均值差异(Maximummeandiscrepancy,MMD)作为一种能有效度量源域和目标域分布差异的标准已被成功运用.然而,MMD作为一种全局度量方法一定程度上反映的是区域之间全局分布和全局结构上的差异.为此,本文通过引入局部加权均值的方法和理论到MMD中,提出一种具有局部保持能力的投影最大局部加权均值差异(Projectedmaximumlocalweightedmeandiscrepancy,PMLWD)度量,%从而一定程度上使得PMLWD更能有效度量源域和目标域中局部分块之间的分布和结构上的差异,结合传统的学习理论提出基于局部加权均值的领域适应学习框架(Localweightedmeanbaseddomainadaptationlearningframework,LDAF),在LDAF框架下,衍生出两种领域适应学习方法:LDAF_MLC和LDAF_SVM.最后,通过测试人工数据集、高维文本数据集和人脸数据集来表明LDAF比其他领域适应学习方法更具优势.

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