Sparse and smooth canonical correlation analysis through rank-1 matrix approximation
作者: Abdeldjalil Aïssa-El-BeyAbd-Krim Seghouane
作者单位: 1IMT Atlantique, UMR CNRS 6285 Lab-STICC, Université Bretagne Loire
2Department of Electrical and Electronic Engineering, University of Melbourne
刊名: EURASIP Journal on Advances in Signal Processing, 2017, Vol.2017 (1), pp.1-14
来源数据库: Springer Journal
DOI: 10.1186/s13634-017-0459-y
关键词: Canonical correlation analysisSparse representationRank-1 matrix approximation
英文摘要: Abstract(#br)Canonical correlation analysis (CCA) is a well-known technique used to characterize the relationship between two sets of multidimensional variables by finding linear combinations of variables with maximal correlation. Sparse CCA and smooth or regularized CCA are two widely used variants of CCA because of the improved interpretability of the former and the better performance of the later. So far, the cross-matrix product of the two sets of multidimensional variables has been widely used for the derivation of these variants. In this paper, two new algorithms for sparse CCA and smooth CCA are proposed. These algorithms differ from the existing ones in their derivation which is based on penalized rank-1 matrix approximation and the orthogonal projectors onto the space spanned by...
全文获取路径: Springer  (合作)

  • canonical 标准的
  • correlation 对比
  • through 经过
  • analysis 分析
  • matrix 石基
  • approximation 近似
  • smooth 平滑的