关键词: 深度置信网络（DBN）, 稀疏性, 高斯分布, 压缩感知, 0范数

Abstract: As a popular research field in recent years, deep learning has great application prospects, but there are many problems such as over-fitting, under-fitting, hidden layer number and node number selection. Aiming at the over-fitting problem of deep belief network, based on the theory of compressed sensing and the mathematical properties of zero norm, a sparse deep belief network based on non-means Gaussian distribution function is constructed. The over-fitting problem is solved by adding a sparse regular term in the pre-training phase, further to improve the deep belief network training process. Using the ORL and MINIST two public data sets to verify and analyze the improved scheme, the results show that it has a greater improvement in sparsity and accuracy than the existing improved schemes.

Key words: Deep Belief Network（DBN）, sparsity, Gaussian distribution, compressed sensing, 0 norm