Principal component analysis

In class activities

Author

Ranjeet Utikar

Published

August 20, 2023

Modified

September 5, 2024

Activities

  1. Explore discriminant analysis by following steps given in lda_qda_example.mlx.

  2. Explore PCA analysis by following steps given in pca_example.mlx.

  3. Explore multivariate analysis for process monitoring using the MSPC app (GIEM, 2023).

  4. Explore PLS modeling by following steps given in pls_example.mlx.

Reference

GIEM (2023). A benchmark software for MSPC (https://www.mathworks.com/matlabcentral/fileexchange/47169-a-benchmark-software-for-mspc), MATLAB Central File Exchange. Retrieved August 27, 2023.

Citation

BibTeX citation:
@online{utikar2023,
  author = {Utikar, Ranjeet},
  title = {Principal Component Analysis},
  date = {2023-08-20},
  url = {https://amc.smilelab.dev//content/notes/06-principal_component_analysis/in-class-activities.html},
  langid = {en}
}
For attribution, please cite this work as:
Utikar, Ranjeet. 2023. “Principal Component Analysis.” August 20, 2023. https://amc.smilelab.dev//content/notes/06-principal_component_analysis/in-class-activities.html.