A novel orthogonal constraint-based subspace learning method for electronic nose drift compensation

Danhong Yi; Zhe Li; Yuan Chen; Jia Yan

doi:10.1117/12.2680198

31 May 2023 A novel orthogonal constraint-based subspace learning method for electronic nose drift compensation

Danhong Yi, Zhe Li, Yuan Chen, Jia Yan

Proceedings Volume 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023); 127043L (2023) https://doi.org/10.1117/12.2680198
Event: 8th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2023), 2023, Hangzhou, China

Abstract

For the sensor drift problem in electronic nose systems, we propose a novel orthogonal constraint-based subspace learning technique in this study. First, the method minimizes the central distance between two domains and preserves the geometric structure of data in the subspace after projection. Second, a linear regression function based on the l_2,1 norm is used to represent the mapping relationship between the subspace and the label space, allowing the correlation relationship between data before and after projection to be maintained, feature extraction ability to be improved, and noise robustness to be achieved. Third, the orthogonal constraint is utilized to encourage geometric interpretation and data reconstruction. Finally, we conduct experiments on typical sensor drift datasets with long-term drift, and the results demonstrate the effectiveness of the method.

Citation Download Citation

Danhong Yi, Zhe Li, Yuan Chen, and Jia Yan "A novel orthogonal constraint-based subspace learning method for electronic nose drift compensation", Proc. SPIE 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023), 127043L (31 May 2023); https://doi.org/10.1117/12.2680198

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available