Subspace clustering guided unsupervised feature selection

作者：

Highlights：

• A novel subspace clustering guided unsupervised feature selection (SCUFS) model is proposed.

• SCUFS learns a similarity graph by self-representation of samples and can uncover the underlying multi-subspace structure of data.

• The iterative updating of similarity graph and pseudo label matrix can learn a more accurate data distribution.

摘要

Highlights•A novel subspace clustering guided unsupervised feature selection (SCUFS) model is proposed.•SCUFS learns a similarity graph by self-representation of samples and can uncover the underlying multi-subspace structure of data.•The iterative updating of similarity graph and pseudo label matrix can learn a more accurate data distribution.

论文关键词：Subspace clustering,Unsupervised feature selection,Spectral clustering,Group sparsity

论文评审过程：Received 22 June 2016, Revised 4 January 2017, Accepted 9 January 2017, Available online 21 January 2017, Version of Record 12 March 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.01.016