A Feature Extraction Method Based on Stacked Denoising Autoencoder for Massive High Dimensional Data
Baoding Xu, Xiangqian Ding, Ruichun Hou, Cheng Zhu · 2018
Massive high dimensional data has a large sample size and high dimensionality. However, all the features of the massive high dimensional data are used for identification or classification, which will increase the calculation time and reduce the accuracy of identification or classification. Therefore, extracting features with strong expressive ability is very important for processing massive high-dimensional data. In order to solve this problem, this paper presents a feature extraction method which based on Stacked Denoising Autoencoder (SDAE). SDAE trains each layer of neural network by unsupervised layer-by-layer greedy training. Then supervised training Softmax classifier. And finally uses Back Propagation (BP) algorithm to optimize the entire model. In this paper, the ISOLET data set is taken as experimental data. The experimental results demonstrated that our proposed method can extract feature subsets with strong expressive ability. And using this feature subset for classification, the classification accuracy is significantly improved.