With correlated inputs, one must solve for both simultaneously which is a much harder problem. This method can be applied to compute the principal eigenvector and eigenvalue of H by the power method. Principal Component Neural Networks . Wiley, New York, 1996. PCA using Support Vector kernel functions.
Extensive simulations show the utility of our approach.
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.