Non-linear matrix completion
作者：
Highlights：
• Conventional matrix completion methods are linear methods. This paper proposed a non-linear matrix completion (NLMC) method that is able to handle data of non-linear structures and high-rank matrices.
• NLMC significantly outperforms existing methods in the tasks of image inpainting and single-/multi-label classification.
• The idea of NLMC is extended to a non-linear rank-minimization framework applicable to other problems such as non-linear denoising.
摘要
•Conventional matrix completion methods are linear methods. This paper proposed a non-linear matrix completion (NLMC) method that is able to handle data of non-linear structures and high-rank matrices.•NLMC significantly outperforms existing methods in the tasks of image inpainting and single-/multi-label classification.•The idea of NLMC is extended to a non-linear rank-minimization framework applicable to other problems such as non-linear denoising.
论文关键词：Matrix completion,Low-rank,Kernel,Schatten p-norm,Image inpainting,Single-/multi-label classification,Non-linear denoising
论文评审过程：Received 26 April 2017, Revised 4 September 2017, Accepted 6 October 2017, Available online 12 October 2017, Version of Record 6 February 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.10.014
