迁移组概率学习机

摘要

基于组概率的学习方法因其能够很好地保护数据的隐私性而成为近年来机器学习领域的研究热点。已有的组概率学习方法虽然取得了一定的效果，但是在模型训练时仅考虑单一的场景信息，如果在当前领域所采集的数据信息有限，则在当前领域下建立的分类模型泛化能力较差。针对此问题，提出了一种基于组概率和结构风险最小化模型的迁移组概率学习机（TGPLM ）。该方法通过构造领域相似距离项来引入历史领域的先验知识，提出了针对类标签保护数据的增强型分类器优化目标学习准则，以期在有效利用当前领域数据类标签组概率信息的同时借鉴历史领域相关知识来指导当前领域下的学习任务。基于模拟、UCI及PIE人脸等数据集上的实验结果表明，本文所提之方法是有效的。%Learning from group probabilities helps to protect the privacy of users and has become a hot topic in the commu-nity of machine learning .The traditional group probabilities based learning methods have gained certain success ,however ,they still fall short when the prior information are not fully provided .In order to solve this problem ,a novel transfer learning method called transfer group probabilities based learning machine (TGPLM in abbreviation ) is proposed by integrating group probabilities into the principle of structure risk minimization .In TGPLM ,a novel learning criteria is proposed based on reusing the related domain knowl-edge by minimizing domain similarity distance ,which makes the proposed TGPLM not only make full use of the group probabilities in the current scene ,but also learn the existing useful knowledge in the history scene effectively .Experimental results on the artifi-cial ,UCI and PIE face datasets show the effectiveness of the proposed method .