It is common practice to preprocess data by extracting linear or nonlinear features. It aims to nd an orthonormal, ordered basis such that the i th direction describes as much variance as possible while maintaining orthogonality to all other directions. K.I. Diamantaras and S.Y. Kung. Principal Component Neural Networks . Wiley, New York, 1996.
Training is done by conjugate gradient descent.
In all algorithms, parameter values were selected such that the best possible denoising result was obtained. 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.