Hessenberg Elm Autoencoder Kernel For Deep Learning

Authors : Gokhan ALTAN, Yakup KUTLU

Pages : 141-151

Doi:10.30931/jetas.450252

View : 16 | Download : 7

Publication Date : 2018-08-30

Article Type : Research Paper

Abstract :Deep Learning insert ignore into journalissuearticles values(DL); is an effective way that reveals on computation capability and advantage of the hidden layer in the network models. It has pre-training phases which define the output parameters in unsupervised ways and supervised training for optimization of the pre-defined classification parameters. This study aims to perform high generalized fast training for DL algorithms with the simplicity advantage of Extreme Learning machines insert ignore into journalissuearticles values(ELM);. The applications of the proposed classifier model were experimented on RespiratoryDatabase@TR. Hilbert-Huang Transform was applied to the 12-channel lung sounds for analyzing amplitude-time-frequency domain. The statistical features were extracted from the intrinsic mode function modulations of lung sounds. The feature set was fed into the proposed Deep ELM with the HessELM-AE. The proposed model was structured with 2 hidden layers insert ignore into journalissuearticles values(340,580 neurons); to classify the lung sounds for separating Chronic Obstructive Pulmonary Disease and healthy subjects. The classification performance was tested using 6-fold cross-validation with proposed Deep. HessELM-AE has achieved an influential accuracy rate of 92.22% whereas the conventional ELM-AE has reached an accuracy rate of 80.82%.
Keywords : Deep Learning, RespiratoryDatabase@TR, COPD, Lung sounds, Deep ELM, Hessenberg decomposition