关键词: 特征学习, 深度学习, 人工神经网络, 栈式去噪自动编码器, 边际Fisher分析

Abstract: Feature learning is a key issue in the field of pattern recognition. By unsupervised pre-trainning and supervised fine-tuning, the deep neural network based on autoencoders can effectively extract critical information of data to form features. A marginal Fisher analysis algorithm based on stacked denoising autoencoders has been proposed which can further improve the ability of representation learning by applying marginal Fisher analysis to the supervised fine-tuning phase. Experimental results show that the algorithm achieves better recognition results compared to the standard stacked denoising autoencoders and the deep belief networks based on restricted Boltzmann machine.

Key words: feature learning, deep learning, artificial neural network, stacked denoising autoencoders, marginal Fisher analysis