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Authors: Heng Ma ; Yonggang Lu and Haitao Zhang

Affiliation: Lanzhou University, China

ISBN: 978-989-758-201-1

Keyword(s): Deep Learning, Autoencoder, Architecture Optimization, Correlation Analysis.

Related Ontology Subjects/Areas/Topics: Adaptive Architectures and Mechanisms ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

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.

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Paper citation in several formats:
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

@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},
}

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

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