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Standardization of near infrared spectra based on multi-task learning.
- Source :
-
Spectroscopy Letters . 2016, Vol. 49 Issue 1, p23-29. 7p. - Publication Year :
- 2016
-
Abstract
- In order to model the near infrared spectral difference between two instruments, this paper presents an approach based on multi-task learning for multivariate instrument standardization. A multi-task learning approach using trace norm regularization is employed to estimate the transformation matrix in direct standardization, and then is extended to the construction of the nonlinear transformation. The proposed approach is compared with the piecewise direct standardization (PDS) on two real data sets. Experimental results show that the proposed approach sometimes outperforms the conventional PDS method, and the multi-task learning method can be a promising way to overcome the over-fitting problem existing in direct standardization. [ABSTRACT FROM PUBLISHER]
- Subjects :
- *STANDARDIZATION
*INFRARED spectra
*BIG data
*T-matrix
*MULTIVARIATE analysis
Subjects
Details
- Language :
- English
- ISSN :
- 00387010
- Volume :
- 49
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Spectroscopy Letters
- Publication Type :
- Academic Journal
- Accession number :
- 110811039
- Full Text :
- https://doi.org/10.1080/00387010.2015.1055770