Deep Learning of Part-Based Representation of Data Using Sparse Autoencoders With Nonnegativity Constraints

We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (nonnegativity-constrained autoencoder), that learns features that show part-based representation of data. The learning algorithm is based on constraining negative weights. The performance of the...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transaction on neural networks and learning systems Vol. 27; no. 12; pp. 2486 - 2498
Main Authors:	Hosseini-Asl, Ehsan, Zurada, Jacek M., Nasraoui, Olfa
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.12.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Artificial neural networks Autoencoder Constraints Cost function Datasets deep architectures Deep learning Encoding Feature extraction feature learning Image reconstruction Machine learning Neural networks nonnegativity constraints part-based representation Performance prediction Representations Training
ISSN:	2162-237X, 2162-2388, 2162-2388
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!