Structure preservation and distribution alignment in discriminative transfer subspace learning
作者: Ting XiaoPeng LiuWei ZhaoHongwei LiuXianglong Tang
作者单位: 1Harbin Institute of Technology, School of Computer Science and Technology, 92 West Dazhi Street, Harbin 150001, China
刊名: Neurocomputing, 2019, Vol.337 , pp.218-234
来源数据库: Elsevier Journal
DOI: 10.1016/j.neucom.2019.01.069
关键词: Transfer learningDomain adaptationLow-rank representationStructure preservationDistribution alignment
原始语种摘要: Abstract(#br)Domain adaptation (DA) is one of the most promising techniques for leveraging existing knowledge from a source domain and applying it to a related target domain. Most DA methods mainly focus on learning a common subspace for the two domains by exploiting either the statistical property or the geometric structure independently to reduce the domain distribution difference. However, these two properties are complementary to each other, and jointly exploring them could yield optimal results. Inspired by the theoretical results of DA, in this paper, we propose structure preservation and distribution alignment (SPDA) in discriminative transfer subspace learning, which embeds the source domain classification error and reduction and domain distribution alignment into a single...
全文获取路径: Elsevier  (合作)
影响因子:1.634 (2012)

  • discriminative 有辨别力的
  • subspace 子空间
  • distribution 分布
  • geometric 凡何
  • learning 学识
  • domain 领域
  • regularization 正则化
  • alignment 定位
  • minimization 最小化
  • framework 构架