Neural network learning to non-linear principal component analysis

An approach to non-linear principal component analysis (NPCA) has been developed. First, a new formulation of the NPCA problem is proposed by combining the essential properties of linear principal component analysis, least squares approximation property and structure preservation property. This formulation ensures that the proposed approach provides robust results in exploratory data analysis. Second, a new neural network learning algorithm for multi-layer feedforward networks, which allows the network inputs to be updated, is proposed to accomplish NPCA in a flexible and adaptive manner. Experimental investigations with simulated and real chemical data on the behavior of the proposed approach are presented in this paper.