计算机应用研究2017，Vol.34Issue(12)：3552-3555,3572,5.DOI:10.3969/j.issn.1001-3695.2017.12.007
层次K-均值聚类结合改进ITML的迁移度量学习方法
Transfer metric learning method based on hierarchical K-means clustering and improved ITML
摘要
Abstract
Now most of transfer learning methods suffer from the problems that transfer types are separately analyzed,low level feature space are used,and the source data set is more diverse and complex than the target set.For these problems,this paper proposed a novel general transfer metric learning framework with comprehensive consideration of feature representation transfer,parameter transfer and instance transfer.Initially,it used hierarchical K-means clustering to get the similarity based on the semantic similarity space and category similarity space.Then,it utilized the trust evaluation framework and de-correlated normalized space to eliminate the correlation learned in the source domain,and restrained the negative transfer.Finally,it modified the information theoretic metric learning to precede similarity metric learning.The experiment results show that the transfer learning performance of the proposed method has improved greatly with more robust to negative transfer effect comparing with the traditional methods in three data sets with different complexity.Furthermore,the proposed method could be applied in the situation that the source data set was simpler than the target set.The results reveal that even when the knowledge source is limited,transfer learning can still be beneficial.关键词
迁移度量学习/层次K-均值聚类/相似性空间/信任评估框架/去相关归一化空间/信息理论度量学习Key words
transfer metric learning/hierarchical K-means clustering/similarity space/trust evaluation framework/de-correlated normalized space/information theoretic metric learning (ITML)分类
信息技术与安全科学引用本文复制引用
蒋林利,吴建生..层次K-均值聚类结合改进ITML的迁移度量学习方法[J].计算机应用研究,2017,34(12):3552-3555,3572,5.基金项目
国家自然科学基金资助项目(61202143) （61202143）
广西自然科学基金资助项目(2014GXNSFAA118027) （2014GXNSFAA118027）
