Nonlinear | Principal Component Analysis And Rela...

Because the bottleneck layer contains fewer nodes than the input or output layers, the network is forced to compress the data. The values extracted at this bottleneck represent the nonlinear principal component scores.

The most widely used implementation of NLPCA involves a multi-layer feed-forward neural network trained to perform an identity mapping. Nonlinear Principal Component Analysis and Rela...

Nonlinear transfer functions (like hyperbolic tangents) in the hidden layers empower the network to characterize arbitrary continuous curves. 2. Principal Curves and Manifolds Because the bottleneck layer contains fewer nodes than