K. I. Diamantaras and S. Y. Kung, Principal Component Neural Networks , Wiley, New York, 1996. Principal Component Neural Networks . Wiley, New York, 1996. This work presents ideas for finding approximate pre images, focusing on Gaussian kernels, and shows experimental results using these pre images in data reconstruction and de noising on toy examples as well as on real world data. The coordinates in the Eigenvector basis are called principal components.
Training is done by conjugate gradient descent.
In all algorithms, parameter values were selected such that the best possible de noising result was obtained. K.I. Diamantaras and S.Y. Kung. Principal Component Neural Networks . Wiley, New York, 1996. To illustrate the feasibility, we then consider an example of de noising Gaussian clusters, and conclude with a discussion.