Discriminative dimensionality reduction for sensor drift compensation in electronic nose: A robust, low-rank, and sparse representation method

作者：

Highlights：

• Propose a robust, low-rank, and sparse representation of gas sensor signals.

• Employ the source data label information to avoid overlapping of samples.

• Solve the formulated problem in an iterative manner.

• Show the performance of the proposed method on two sensor drift datasets.

摘要

•Propose a robust, low-rank, and sparse representation of gas sensor signals.•Employ the source data label information to avoid overlapping of samples.•Solve the formulated problem in an iterative manner.•Show the performance of the proposed method on two sensor drift datasets.

论文关键词：Sensor drift, electronic nose,Dimensionality reduction,Domain adaptation,Transfer learning,Low-rank and sparse representation

论文评审过程：Received 11 March 2019, Revised 26 November 2019, Accepted 22 January 2020, Available online 23 January 2020, Version of Record 31 January 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113238