Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

Title
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia
Authors
Keywords
Deep learning, Functional connectivity, Resting-state functional magnetic resonance imaging, Schizophrenia, Sparsity, Stacked autoencoder
Journal
NEUROIMAGE
Volume 124, Issue -, Pages 127-146
Publisher
Elsevier BV
Online
2015-05-21
DOI
10.1016/j.neuroimage.2015.05.018

Ask authors/readers for more resources

Reprint

Contact the author

Add your recorded webinar

Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.

Upload Now

Create your own webinar

Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.

Create Now