Initially proposed by Hastie and Stuetzle, principal curves are smooth, self-consistent curves that pass through the "middle" of a data cloud. Unlike the rigid orthogonal vectors of linear PCA, a principal curve bends and twists to accommodate the global shape of the data. 3. Kernel PCA (kPCA)
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. Nonlinear Principal Component Analysis and Rela...
To accomplish this, three primary methodologies have emerged over the decades: 1. Autoassociative Neural Networks (Autoencoders) Initially proposed by Hastie and Stuetzle, principal curves
The most widely used implementation of NLPCA involves a multi-layer feed-forward neural network trained to perform an identity mapping. Initially proposed by Hastie and Stuetzle