Fisher Discriminant Analysis Based on Stacked AutoEncoders

Among the methods of common-featured extraction, Fisher Discriminant Analysis(FDA) can only extract the linear features. While the method based on kernel has the ability to extract nonlinear features, but it is very sensitive to the type of kernel function and its parameters. The effective feature extraction of data is concerned in this paper. A feature extraction algorithm based on Stacked AutoEncoders(SAE) and FDA is proposed. The training process of the deep-learning network model in this algorithm combines unsupervised features extracted by SAE with supervised features extracted by FDA. Through the comparison of features extracted from multilayer automatic encoding machine, extreme learning machine (Extreme Learning Machine, ELM) and other models, by using support vector machine to classify the data features in the data set Pendigits, MNIST, ORL, AR, FDA based on SAE (FDA-SAE) has good performance in the classification accuracy and runtime. Especially in small dataset AR, the effect is especially obvious under the condition of less sample features.