Riemannian dynamic generalized space quantization learning

作者：

Highlights：

• We innovatively propose to represent each instance in the task of EEG classification by a sequence of SPD matrices, instead of single one.

• We propose a novel classification method that can directly deal with data represented by sequences of SPD matrices via Riemannian geometry.

• The sequential representation shows superior performance on both synthetic and real-world data sets.

摘要

•We innovatively propose to represent each instance in the task of EEG classification by a sequence of SPD matrices, instead of single one.•We propose a novel classification method that can directly deal with data represented by sequences of SPD matrices via Riemannian geometry.•The sequential representation shows superior performance on both synthetic and real-world data sets.

论文关键词：Learning vector quantization,Dynamic learning vector quantization,Riemannian manifold,Short-term memory

论文评审过程：Received 5 January 2022, Revised 30 June 2022, Accepted 21 July 2022, Available online 23 July 2022, Version of Record 28 July 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108932