276
Views
0
CrossRef citations to date
0
Altmetric
Quality Quandaries

Hidden dimensions of the data: PCA vs autoencoders

&

References

  • Ban, X., J. Lucas, S. Sachdeva, and R. Grosse. 2020. Regularized linear autoencoders recover the principal components, eventually. In 34th Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada.
  • Bisgaard, B., and M. Kulahci. 2005. The effect of autocorrelation on statistical process control procedures. Quality Engineering 17 (3):481–9. doi:10.1081/QEN-200068575.
  • Bisgaard, B., and M. Kulahci. 2006. The application of principal component analysis for process monitoring. Quality Engineering 18 (1):95–103. doi:10.1080/08982110500403565.
  • Bishop, C. M. 2006. Pattern recognition and machine learning. New York: Springer.
  • Cacciarelli, D., and M. Kulahci. 2022. A novel fault detection and diagnosis approach based on orthogonal autoencoders. Computers & Chemical Engineering 163:107853. doi:10.1016/j.compchemeng.2022.107853.
  • Cacciarelli, D., M. Kulahci, and J. Tyssedal. 2022. Online active learning for soft sensor development using semi-supervised autoencoders. In ICML 2022 Workshop on Adaptive Experimental Design and Active Learning in the Real World, Baltimore, MD. https://arxiv.org/abs/2212.13067.
  • Eckart, C., and G. Young. 1936. The approximation of one matrix by another of lower rank. Psychometrika 1 (3):211–8. doi:10.1007/BF02288367.
  • Ferrer, A. 2014. Latent structures-based multivariate statistical process control: A paradigm shift. Quality Engineering 26 (1):72–91. doi:10.1080/08982112.2013.846093.
  • Fortuna, L., S. Graziani, A. Rizzo, and M. G. Xibilia. 2007. Soft sensors for monitoring and control of industrial processes. New York: Springer.
  • Gajjar, S., M. Kulahci, and A. Palazoglu. 2018. Real-time fault detection and diagnosis using sparse principal component analysis. Journal of Process Control 67:112–28. doi:10.1016/j.jprocont.2017.03.005.
  • Gajjar, S., M. Kulahci, and A. Palazoglu. 2020. Least squares sparse principal component analysis and parallel coordinates for real-time process monitoring. Industrial & Engineering Chemistry Research 59 (35):15656–70. doi:10.1021/acs.iecr.0c01749.
  • Jolliffe, I. T. 2002. Principal component analysis. New York: Springer-Verlag. doi:10.1007/b98835.
  • Kingma, D. P., and J. Ba. 2014. Adam: A method for stochastic optimization.
  • Kunin, D., J. M. Bloom, A. Goeva, and C. Seed. 2019. Loss landscapes of regularized linear autoencoders. In Proceedings of the 36th International Conference on Machine Learning (ICML).
  • Narkhede, M. V., P. P. Bartakke, and M. S. Sutaone. 2022. A review on weight initialization strategies for neural networks. Artificial Intelligence Review 55 (1):291–322. doi:10.1007/s10462-021-10033-z.
  • Wang, W., D. Yang, F. Chen, Y. Pang, S. Huang, and Y. Ge. 2019. Clustering with orthogonal AutoEncoder. IEEE Access 7:62421–32. doi:10.1109/ACCESS.2019.2916030.
  • Yin, J., and X. Yan. 2021. Stacked sparse autoencoders monitoring model based on fault-related variable selection. Soft Computing 25 (5):3531–43. doi:10.1007/s00500-020-05384-8.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.