Nonlinear Principal Component Analysis And Rela... May 2026

To accomplish this, three primary methodologies have emerged over the decades: 1. Autoassociative Neural Networks (Autoencoders)

The network typically utilizes five layers: an input layer, an encoding layer, a narrow "bottleneck" layer, a decoding layer, and an output layer. Nonlinear Principal Component Analysis and Rela...

Traditional PCA finds the lower-dimensional hyperplane that minimizes the sum of squared orthogonal deviations from the dataset. In contrast, NLPCA maps the data to a lower-dimensional curved surface. To accomplish this, three primary methodologies have emerged

The most widely used implementation of NLPCA involves a multi-layer feed-forward neural network trained to perform an identity mapping. To accomplish this