深度信念网络的隐含神经元大部分为噪声变量,且具有组结构相关性.组稀疏深度信念网络模型通过组Lasso模型对隐含神经元变量进行约束,从而实现变量组选择.然而,组稀疏深度信念网络模型未能考虑特征可同时属于多个特征组,并且隐含神经元在变量层面上不稀疏的问题.在组稀疏深度信念网络模型上引入重叠组结构,解释了重叠组Lasso模型在变量层面上比组Lasso模型稀疏的原因,并在变量层面上作进一步的稀疏,提出了重叠稀疏组深度信念网络模型.在MNIST、USPS、ETH-80以及人脸数据集上的识别结果表明,重叠稀疏组深度信念网络具有更高的识别率.%Most hidden neurons in Deep Belief Network (DBN) are noise variables,and have group structure correlation.Group Sparse Deep Belief Network (GSDBN) constrains the implicit neuron variables via group Lasso so as to achieve variable group selection.However,the group sparse model not only ignores the case that some features belong to multiple groups simultaneously,but also has the problem that hidden neurons are not sparse.In this paper,we propose Overlap Sparse Group Deep Belief Network (OSGDBN),which introduces the overlap group structure and makes the hidden neurons sparse,based on Group Sparse Deep Belief Network.In addition,we also explain the reason that the OSGDBN is sparser than GSDBN.The recognition results on MNIST,USPS,ETH-80 and face datasets show that OSGDBN has a higher recognition rate.
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