Deep learning classifier based on NPCA and orthogonal feature selection
Aleksy Stanisław Barcz
In this paper the idea of deep learning classifier is developed. The effectiveness of discriminative classifier, as e.g.
multilayer perceptron, support vector machine can be improved by adding the data preprocessing blocks: orthogonal
feature selection (Gram-Schmidt method) and nonlinear principal component analysis. We present the case study of various
structures of deep learning systems (scenarios).