一种基于局部加权均值的领域适应学习框架

摘要

Maximum mean discrepancy (MMD),as a criterion effectively and efficiently measuring the distribution discrepancy between source domains and target ones,has been successfully used.But it is a global measuring algorithm and to some extent only reflects the global distribution discrepancy between domains and the global structural difference.Therefore,we propose projected maximum local weighted mean discrepancy (PMLWD) scheme by with locality preserving ability integrating the theory and method of local weighted mean into the MMD.At the same time,we formulate in theory that the PMLWD is one of generalized algorithms of the MMD.Furthermore,on the basis of the PMLWD and by integrating classical learning theories,we present local weighted mean based domain adaptation learning framework (LDAF).Following the LDAF,we propose local weighted mean based multi-label classification domain adaptation learning algorithm (LDAF_MLC) and local weighted mean based domain adaptation supporting vector machine (LDAF_SVM).At last,tests on artificial data sets,high dimensional text data sets and face data sets show the LDAF methods are superior to other domain adaption ones.%最大均值差异(Maximum mean discrepancy,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(Projected maximumlocal weighted mean discrepancy,PMLWD)度量,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(Local weighted mean based domain adaptation learning framework,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.