Nonlinear dimensionality reduction with input distances preservation

A new error term for dimensionality reduction, which clearly improves the quality of nonlinear principal component analysis neural networks, is introduced, and some illustrative examples are given. The method maintains the original data structure by preserving the distances between data points.