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Non linear principal component analysis


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There are multiple methods for doing this. Here is one I prefer.

  1. Start with your features.
  2. Set error to your features.
  3. Train two models, one that encodes error into 1 value, and another that turns that 1 value into error. This step is essentially an auto-encoder.
  4. Take the difference of the predictions and error, this becomes the new error.
  5. Do steps 3 and 4 for N iterations to end up with N principal components.

This gives us something like a PCA, or a greedy auto-encoder.

The following pages link here

Citation

If you find this work useful, please cite it as: 
@article{yaltirakli,
  title   = "Non linear principal component analysis",
  author  = "Yaltirakli, Gokberk",
  journal = "gkbrk.com",
  year    = "2025",
  url     = "https://www.gkbrk.com/non-linear-principal-component-analysis"
}
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IEEE Citation
Gokberk Yaltirakli, "Non linear principal component analysis", January, 2025. [Online]. Available: https://www.gkbrk.com/non-linear-principal-component-analysis. [Accessed Jan. 25, 2025].
APA Style
Yaltirakli, G. (2025, January 25). Non linear principal component analysis. https://www.gkbrk.com/non-linear-principal-component-analysis
Bluebook Style
Gokberk Yaltirakli, Non linear principal component analysis, GKBRK.COM (Jan. 25, 2025), https://www.gkbrk.com/non-linear-principal-component-analysis

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