Multi-view low-rank dictionary learning for image classification
作者：
Highlights：
• We offer a multi-view low-rank dictionary learning method for image classification.
• Multi-view dictionary low-rank regularization term is designed to handle noise.
• Structural incoherence constraint is given to reduce redundancy in dictionaries.
• Multi-view collaborative representation based classification scheme is provided.
• Effectiveness and efficiency of our method are demonstrated on four datasets.
摘要
•We offer a multi-view low-rank dictionary learning method for image classification.•Multi-view dictionary low-rank regularization term is designed to handle noise.•Structural incoherence constraint is given to reduce redundancy in dictionaries.•Multi-view collaborative representation based classification scheme is provided.•Effectiveness and efficiency of our method are demonstrated on four datasets.
论文关键词：Multi-view dictionary learning,Multi-view dictionary low-rank regularization,Structural incoherence constraint,Collaborative representation based classification
论文评审过程：Received 17 October 2014, Revised 25 May 2015, Accepted 13 August 2015, Available online 21 August 2015, Version of Record 5 November 2015.
论文官网地址：https://doi.org/10.1016/j.patcog.2015.08.012
