An Algorithm for Learning Principal Curves with Principal Component Analysis and Back-Propagation Network

A new algorithm for learning principal curves with definite mathematical representations is proposed based on combining the principal component analysis (PCA) and back-propagation (BP) network. The algorithm successfully turns an unsupervised learning problem into a supervised one by projecting a data set to its first component line and identifying the relation between the data points and their corresponding projection indices with BP network. This algorithm has been proved distinctly superior to the HS algorithm.