2016
Integration of orthogonal feature detectors in parameter learning of artificial neural networks to improve robustness and the evaluation on hand-written digit recognition tasks
ICASSP 2016accepted
We propose to use orthogonal feature detectors in artificial neural networks for the robustness of performance under noisy conditions. The motivation is grounded on the principle that orthogonal decomposition is the most efficient among all representation of a signal. In this paper, we incorporate o…