References
Ge Z Q. Review on data-driven modeling and monitoring for plant-wide industrial processes. Chemometr Intell Laboratory Syst, 2017, 171: 16–25
Ge Z Q, Song Z, Ding S X, et al. Data mining and analytics in the process industry: the role of machine learning. IEEE Access, 2017, 5: 20590–20616
Tang Q, Chai Y, Qu J, et al. Fisher discriminative sparse representation based on DBN for fault diagnosis of complex system. Appl Sci, 2018, 8: 795
Huang S, Elgammal A, Huangfu L, et al. Globality-locality preserving projections for biometric data dimensionality reduction. In: Proceedings of Computer Vision and Pattern Recognition Workshops. New York: IEEE, 2014. 15–20
Zhan C J, Li S H, Yang Y P. Enhanced fault detection based on ensemble global-local preserving projections with quantitative global-local structure analysis. Ind Eng Chem Res, 2017, 56: 10743–10755
He X. Locality preserving projections. Adv Neural Inf Process Syst, 2003, 16: 186–197
Clemmensen L, Hastie T, Witten D, et al. Sparse discriminant analysis. Technometrics, 2011, 53: 406–413
Yu W, Zhao C. Sparse exponential discriminant analysis and its application to fault diagnosis. IEEE Trans Ind Electron, 2018, 65: 5931–5940
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant Nos. 61773080, 61673076, 61633005).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tang, Q., Li, B., Chai, Y. et al. Improved sparse representation based on local preserving projection for the fault diagnosis of multivariable system. Sci. China Inf. Sci. 64, 129204 (2021). https://doi.org/10.1007/s11432-018-9613-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11432-018-9613-2