Orthogonal transformation of output principal components for improved tolerance to error

Preprocessing of data to be learned by a neural network is typically done to improve neural network performance. Output processing is especially important since it directly affects the influence of error in the hidden layers on the error of the neural network output. Principal component analysis is a commonly used preprocessing method that can improve the network performance by reducing the output dimensionality and reducing the number of parameters in a neural network model. Transforming the principal components of the outputs with an orthonormal matrix prior to scaling can further improve network performance.