Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-24 Cooperative journals: 《计算机应用研究》
Abstract： Auto-Encoder can express the semantic features of data through deep unsupervised learning, but it is hard to determine the nodes of hidden layer and the processing of data for classification often leads to low accuracy and low stability. To solve the problems, this paper proposes a semi-supervised auto-encoder using sparse and label regularizations (LSRAE) to extract the essential features of the samples more accurately by combining unsupervised learning with supervised learning. The sparse regularization term adds constraints to the response of each hidden node, so that this algorithm can find potential structures in the data when the number of hidden neurons is large. At the same time, this algorithm introduce a label regularization term to compare the actual labels with desired labels by supervised learning to adjust the network parameters and further improve the classification accuracy. In order to verify the validity of the proposed method, this algorithm tests many data sets in the experiment. The results show that compared with traditional auto-encoders (AE) , sparse auto-encoder (SAE) , and extreme learning machine (ELM) , SLRAE can obviously improve the classification accuracy and stability when the processed data is applied to the same classifier.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-20 Cooperative journals: 《计算机应用研究》
Abstract： Due to the difference of handwriting caused by the large differences in edge contour, this paper proposed an algorithm named Jacobian regularized sparse automatic encoding machine (JSAE) for handwriting identification. This algorithm added sparse constraint and Jacobi regular item into the automatic coding machine, which improves the recognition accuracy. The sparse constraint can extract hidden structure from the data effectively and the regularized Jacobi can describe the marginal features of point data, thus it enables the learning ability of auto-encoder algorithm to improve and obtain the essential characteristics of the sample more accurately. Experimental results show that JSAE outperforms the basic auto-encoders (AE) and sparse auto-encoders(SAE) .
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