Classification of symmetric positive definite matrices based on bilinear isometric Riemannian embedding

作者：

Highlights：

• Bilinear isometric mapping is proposed to extract embedding of Riemannian manifold.

• The proposed method can maximize the preservation of Riemannian geodesic distance.

• A supervised classification algorithm based on extracted embedding is proposed.

• High performances of proposed methods are supported by the experimental results.

摘要

•Bilinear isometric mapping is proposed to extract embedding of Riemannian manifold.•The proposed method can maximize the preservation of Riemannian geodesic distance.•A supervised classification algorithm based on extracted embedding is proposed.•High performances of proposed methods are supported by the experimental results.

论文关键词：Covariance feature,Dimensionality reduction,Isometric projection,Riemannian manifold,Pattern classification

论文评审过程：Received 18 August 2016, Revised 18 September 2018, Accepted 9 October 2018, Available online 10 October 2018, Version of Record 16 October 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2018.10.009