Deep learning classifier based on NPCA and orthogonal feature selection

Stanisław Jankowski; Zbigniew Szymański; Uladzimir Dziomin; Vladimir Golovko; Aleksy Barcz

doi:10.1117/12.2249848

Translator Disclaimer

28 September 2016 Deep learning classifier based on NPCA and orthogonal feature selection

Stanisław Jankowski, Zbigniew Szymański, Uladzimir Dziomin, Vladimir Golovko, Aleksy Barcz

Author Affiliations +

Proceedings Volume 10031, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016; 100315E (2016) https://doi.org/10.1117/12.2249848
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016, 2016, Wilga, Poland

Abstract

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).

Citation Download Citation

Stanisław Jankowski, Zbigniew Szymański, Uladzimir Dziomin, Vladimir Golovko, and Aleksy Barcz "Deep learning classifier based on NPCA and orthogonal feature selection", Proc. SPIE 10031, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016, 100315E (28 September 2016); https://doi.org/10.1117/12.2249848

ACCESS THE FULL ARTICLE