Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding
作者：
Highlights：
• This paper proposes a novel EMR-SLRA algorithm for multiview feature embedding.
• The least-squares component analysis is generalized to multiview version.
• The ensemble manifold regularization is enforced to explore the complementarity.
• The group sparsity is introduced to promote the robustness against the noise.
• An efficient iterative procedure is developed to solve EMR-SLRA.
摘要
Highlights•This paper proposes a novel EMR-SLRA algorithm for multiview feature embedding.•The least-squares component analysis is generalized to multiview version.•The ensemble manifold regularization is enforced to explore the complementarity.•The group sparsity is introduced to promote the robustness against the noise.•An efficient iterative procedure is developed to solve EMR-SLRA.
论文关键词：Multiview,Feature extraction,Low-rank matrix approximation,Ensemble manifold regularization,Group sparsity
论文评审过程：Received 14 August 2014, Revised 15 November 2014, Accepted 20 December 2014, Available online 2 January 2015, Version of Record 17 June 2015.
论文官网地址：https://doi.org/10.1016/j.patcog.2014.12.016
