CLC number: TP391.9
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2020-06-10
Cited: 0
Clicked: 7047
Citations: Bibtex RefMan EndNote GB/T7714
Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang. Representation learning via a semi-supervised stacked distance autoencoder for image classification[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(7): 1005-1018.
@article{title="Representation learning via a semi-supervised stacked distance autoencoder for image classification",
author="Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="7",
pages="1005-1018",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900116"
}
%0 Journal Article
%T Representation learning via a semi-supervised stacked distance autoencoder for image classification
%A Liang Hou
%A Xiao-yi Luo
%A Zi-yang Wang
%A Jun Liang
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 7
%P 1005-1018
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900116
TY - JOUR
T1 - Representation learning via a semi-supervised stacked distance autoencoder for image classification
A1 - Liang Hou
A1 - Xiao-yi Luo
A1 - Zi-yang Wang
A1 - Jun Liang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 7
SP - 1005
EP - 1018
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900116
Abstract: image classification is an important application of deep learning. In a typical classification task, the classification accuracy is strongly related to the features that are extracted via deep learning methods. An autoencoder is a special type of neural network, often used for dimensionality reduction and feature extraction. The proposed method is based on the traditional autoencoder, incorporating the “distance” information between samples from different categories. The model is called a semi-supervised distance autoencoder. Each layer is first pre-trained in an unsupervised manner. In the subsequent supervised training, the optimized parameters are set as the initial values. To obtain more suitable features, we use a stacked model to replace the basic autoencoder structure with a single hidden layer. A series of experiments are carried out to test the performance of different models on several datasets, including the MNIST dataset, street view house numbers (SVHN) dataset, German traffic sign recognition benchmark (GTSRB), and CIFAR-10 dataset. The proposed semi-supervised distance autoencoder method is compared with the traditional autoencoder, sparse autoencoder, and supervised autoencoder. Experimental results verify the effectiveness of the proposed model.
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