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Nonlinear PCA
  • FAQ
    • complexity (hidden-units, weight-decay)
    • High dim input
    • Optimal k
    • Robustness & reproducibility
    • Loadings (Tangent)
    • Data preprocessing
    • Variance of components
Nonlinear PCA

High dim input

FAQ - Frequently asked questions

High dimensional input data

If data dimension is much larger than the requested number of components k, linear PCA pre-processing of the data is recommended for reducing the dimension of the data before applying nonlinear PCA. This can automatically be done by using the option 'pre_pca'.

Example

data dimension: 20 (or larger)

requested components: k=3

The dimension of the input data will be reduced to 12 by setting this number as required input (and output) units of the nonlinear network structure.

[pc,net,network]=nlpca(data,3, 'pre_pca','yes', 'units_per_layer',[12, 8, 3, 8, 12] );

  FAQ   |   Nonlinear PCA toolbox for MATLAB    |   Matthias Scholz


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