Adaptive maximum margin analysis for image recognition

作者：

Highlights：

• AMMA adaptively selects the nearby points that determine the margin, in the low- dimensional space.

• AMMA can maximize the margin in the low-dimensional space, which is important for classification.

• AMMA adaptively calculates the weights of adjacency graph.

• AMMA has no parameter and fits SRC for classification.

摘要

•AMMA adaptively selects the nearby points that determine the margin, in the low- dimensional space.•AMMA can maximize the margin in the low-dimensional space, which is important for classification.•AMMA adaptively calculates the weights of adjacency graph.•AMMA has no parameter and fits SRC for classification.

论文关键词：Maximum margin,Dimensionality reduction,Sparse representation,Image recognition

论文评审过程：Received 3 December 2015, Revised 14 July 2016, Accepted 15 July 2016, Available online 20 July 2016, Version of Record 21 August 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.07.025