Semi-supervised sparse feature selection based on multi-view Laplacian regularization

作者：

Highlights：

• Multi-view Laplacian sparse feature selection (MLSFS) algorithm is proposed.

• Multi-view learning is utilized to exploit the complementation of different views features.

• A effective iterative algorithm is introduced to optimize the objective function.

• The convergence of the algorithm is proven.

• Experiments demonstrate MLSFS has good performance of feature selection.

摘要

•Multi-view Laplacian sparse feature selection (MLSFS) algorithm is proposed.•Multi-view learning is utilized to exploit the complementation of different views features.•A effective iterative algorithm is introduced to optimize the objective function.•The convergence of the algorithm is proven.•Experiments demonstrate MLSFS has good performance of feature selection.

论文关键词：Multi-view learning,Laplacian regularization,Semi-supervised learning,Sparse feature selection

论文评审过程：Received 2 September 2014, Revised 7 May 2015, Accepted 12 June 2015, Available online 23 June 2015, Version of Record 9 July 2015.

论文官网地址：https://doi.org/10.1016/j.imavis.2015.06.006