Supervised Dictionary Learning With Regularization for Near-Infrared Spectroscopy Classification

Supervised Dictionary Learning With Regularization for Near-Infrared Spectroscopy Classification

Near-infrared spectroscopy (NIRS) has been widely used in many fields due to its advantages with fast analysis speed, non-destructive testing, and on-site detection. However, NIRS has some shortcomings, such as low signal-to-noise ratio, weak absorption intensity, and overlapping peaks. The research...

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Bibliographic Details
Main Authors: Lingqiao Li, Xipeng Pan, Huihua Yang, Tao Zhang, Zhenbing Liu
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Near-infrared spectroscopy
sparse classification
regularization factors
representation-constrained
coefficients incoherence
Online Access:https://ieeexplore.ieee.org/document/8768292/

Internet

https://ieeexplore.ieee.org/document/8768292/

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