无监督混阶栈式稀疏自编码器的图像分类学习

目前多数图像分类的方法是采用监督学习或者半监督学习对图像进行降维，然而监督学习与半监督学习需要图像携带标签信息。针对无标签图像的降维及分类问题，提出采用混阶栈式稀疏自编码器对图像进行无监督降维来实现图像的分类学习。首先，构建一个具有三个隐藏层的串行栈式自编码器网络，对栈式自编码器的每一个隐藏层单独训练，将前一个隐藏层的输出作为后一个隐藏层的输入，对图像数据进行特征提取并实现对数据的降维。其次，将训练好的栈式自编码器的第一个隐藏层和第二个隐藏层的特征进行拼接融合，形成一个包含混阶特征的矩阵。最后，使用支持向量机对降维后的图像特征进行分类，并进行精度评价。在公开的四个图像数据集上将所提方法与七个对比算法进行对比实验，实验结果表明，所提方法能够对无标签图像进行特征提取，实现图像分类学习，减少分类时间，提高图像的分类精度。

Image classification learning via unsupervised mixed-order stacked sparse autoencoder

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.