Robust Low-rank subspace segmentation with finite mixture noise
作者：
Highlights：
• The model first resorts to powerful and intrinsically flexible mixture of exponent power (MoEP) distribution to model complex noise in multiple subspace clustering context.
• The matrix variate elliptically contoured distribution is leveraged as a low-rank component prior.
• A practical algorithm termed MoEP-RSS infers the parameters of MoEP as well as the final clustering results.
摘要
•The model first resorts to powerful and intrinsically flexible mixture of exponent power (MoEP) distribution to model complex noise in multiple subspace clustering context.•The matrix variate elliptically contoured distribution is leveraged as a low-rank component prior.•A practical algorithm termed MoEP-RSS infers the parameters of MoEP as well as the final clustering results.
论文关键词：Subspace clustering,Noises modelling,Finite mixture model,Nonconvex and nonsmooth optimization
论文评审过程：Received 19 October 2017, Revised 23 October 2018, Accepted 27 March 2019, Available online 4 April 2019, Version of Record 16 April 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.03.028
