A deep sparse autoencoder is used to extract features from a typical unsupervised deep learning autoencoder model classified by the Random Forest (RF) classification algorithm ... SA-RF, and DSA-SVC.

In this paper, a novel deep neural network is proposed for emotion classification using EEG systems, which combines the Convolutional Neural Network (CNN), Sparse Autoencoder (SAE ... pooling layer ...

In section 3, we propose the design of the machine-learning algorithm, the feature extraction, and the classification processes ... The bottleneck of the sparse autoencoder is used as input vector to ...