Deep learning classifier based on NPCA and orthogonal feature selection
S. Jankowski, Zbigniew Szymanski, Uladzimir Dziomin, Vladimir A. Golovko, Aleksy Barcz · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
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).