Validation of Nonlinear PCA

@article{Scholz2012ValidationON,
  title={Validation of Nonlinear PCA},
  author={Matthias Scholz},
  journal={Neural Processing Letters},
  year={2012},
  volume={36},
  pages={21-30}
}
  • Matthias Scholz
  • Published 2012
  • Computer Science, Mathematics
  • Neural Processing Letters
  • Linear principal component analysis (PCA) can be extended to a nonlinear PCA by using artificial neural networks. But the benefit of curved components requires a careful control of the model complexity. Moreover, standard techniques for model selection, including cross-validation and more generally the use of an independent test set, fail when applied to nonlinear PCA because of its inherent unsupervised characteristics. This paper presents a new approach for validating the complexity of… CONTINUE READING
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