Determining the Near Optimal Architecture of Autoencoder using Correlation Analysis of the Network Weights

Heng Ma, Yonggang Lu, Haitao Zhang

2016

Abstract

Currently, deep learning has already been successfully applied in many fields such as image recognition, recommendation systems and so on. Autoencoder, as an important deep learning model, has attracted a lot of research interests. The performance of the autoencoder can greatly be affected by its architecture. How-ever, how to automatically determine the optimal architecture of the autoencoder is still an open question. Here we propose a novel method for determining the optimal network architecture based on the analysis of the correlation of the network weights. Experiments show that for different datasets the optimal architecture of the autoencoder may be different, and the proposed method can be used to obtain near optimal network architecture separately for different datasets.

Paper Citation

in Harvard Style

Ma H., Lu Y. and Zhang H. (2016). Determining the Near Optimal Architecture of Autoencoder using Correlation Analysis of the Network Weights . In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 53-61. DOI: 10.5220/0006039000530061

in Bibtex Style

@conference{ncta16,
author={Heng Ma and Yonggang Lu and Haitao Zhang},
title={Determining the Near Optimal Architecture of Autoencoder using Correlation Analysis of the Network Weights},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (IJCCI 2016)},
year={2016},
pages={53-61},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006039000530061},
isbn={978-989-758-201-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (IJCCI 2016)
TI - Determining the Near Optimal Architecture of Autoencoder using Correlation Analysis of the Network Weights
SN - 978-989-758-201-1
AU - Ma H.
AU - Lu Y.
AU - Zhang H.
PY - 2016
SP - 53
EP - 61
DO - 10.5220/0006039000530061