%0 Journal Article %A LIN Minmin %A YANG Donghai %A YANG Jingmin %A ZHANG Wenjie %T Image classification learning via unsupervised mixed-order stacked sparse autoencoder %D 2019 %R 10.11772/j.issn.1001-9081.2019061107 %J Journal of Computer Applications %P 3420-3425 %V 39 %N 12 %X Most of the current image classification methods use supervised learning or semi-supervised learning to reduce image dimension. However, supervised learning and semi-supervised learning require image carrying label information. Aiming at the dimensionality reduction and classification of unlabeled images, a mixed-order feature stacked sparse autoencoder was proposed to realize the unsupervised dimensionality reduction and classification learning of the images. Firstly, a serial stacked sparse autoencoder network with three hidden layers was constructed. Each hidden layer of the stacked sparse autoencoder was trained separately, and the output of the former hidden layer was used as the input of the latter hidden layer to realize the feature extraction of image data and the dimensionality reduction of the data. Secondly, the features of the first hidden layer and the second hidden layer of the trained stacked autoencoder were spliced and fused to form a matrix containing mixed-order features. Finally, the support vector machine was used to classify the image features after dimensionality reduction, and the accuracy was evaluated. The proposed method was compared with seven comparison algorithms on four open image datasets. The experimental results show that the proposed method can extract features from unlabeled images, realize image classification learning, reduce classification time and improve image classification accuracy. %U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2019061107