Nonlinear principal component analysis by neural networks

Nonlinear principal component analysis (NLPCA) can be performed by a neural network model which nonlinearly generalizes the classical principal component analysis (PCA) method. The presence of local minima in the cost function renders the NLPCA somewhat unstable, as optimizations started from different initial parameters often converge to different minima. Regularization by adding weight penalty terms to the cost function is shown to improve the stability of the NLPCA. With the linear approach, there is a dichotomy between PCA and rotated PCA methods, as it is generally impossible to have a solution simultaneously (a) explaining maximum global variance of the data, and (b) approaching local data clusters. With the NLPCA, both objectives (a) and (b) can be attained together, thus the nonlinearity in NLPCA unifies the PCA and rotated PCA approaches. With a circular node at the network bottleneck, the NLPCA is able to extract periodic or wave modes. The Lorenz (1963) 3‐component chaotic system and the monthly tropical Pacific sea surface temperatures (1950–1999) are used to illustrated the NLPCA approach.