Relational autoencoder for feature extraction
- Publication Type:
- Conference Proceeding
- Citation:
- Proceedings of the International Joint Conference on Neural Networks, 2017, 2017-May pp. 364 - 371
- Issue Date:
- 2017-06-30
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 07965877.pdf | Published version | 184.88 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
© 2017 IEEE. Feature extraction becomes increasingly important as data grows high dimensional. Autoencoder as a neural network based feature extraction method achieves great success in generating abstract features of high dimensional data. However, it fails to consider the relationships of data samples which may affect experimental results of using original and new features. In this paper, we propose a Relation Autoencoder model considering both data features and their relationships. We also extend it to work with other major autoencoder models including Sparse Autoencoder, Denoising Autoencoder and Variational Autoencoder. The proposed relational autoencoder models are evaluated on a set of benchmark datasets and the experimental results show that considering data relationships can generate more robust features which achieve lower construction loss and then lower error rate in further classification compared to the other variants of autoencoders.
Please use this identifier to cite or link to this item: