Non-Linear Dimensionality Reduction 论文

摘要

A method for creating a non--linear encoder--decoder for multidimensional data with compact representations is presented. The commonly used technique of autoassociation is extended to allow non--linear representations, and an objective function which penalizes activations of individual hidden units is shown to result in minimum dimensional encodings with respect to allowable error in reconstruction. 1 INTRODUCTION Reducing dimensionality of data with minimal information loss is important for feature extraction, compact coding and computational efficiency. The data can be tranformed into "good" representations for further processing, constraints among feature variables may be identified, and redundancy eliminated. Many algorithms are exponential in the dimensionalityof the input, thus even reduction by a single dimension may provide valuable computational savings. Autoassociating feedforward networks with one hidden layer have been shown to extract the principal components of the data ...