计算机工程与应用2018，Vol.54Issue(7)：213-220,231,9.DOI:10.3778/j.issn.1002-8331.1611-0051
基于Lorentz函数的稀疏约束RBM模型的算法研究
Research on algorithm of sparse constraint RBM model based on Lorentz function
邹维宝 1于昕玉 1麦超2
作者信息
- 1. 长安大学 地质工程与测绘学院,西安710054
- 2. 广西壮族自治区遥感信息测绘院,南宁530023
- 折叠
摘要
Abstract
The Restricted Boltzmann Machine(RBM)is an effective feature extraction algorithm,inspired by the visual cortex sparse representation,people try to introduce the concept of sparse to the RBM,in order to learn to original data sparse representation,improve the capability of feature extraction.The Lorentz function is introduced to the RBM,as the RBM constrained sparse regularization is constructed based on Lorentz function of constrained sparse RBM model,referred to as LRBM(Lorentz function-based sparse constraints RBM).The feature extraction performance of the model is evalu-ated visually, and the sparsity and classification rate are analyzed. Finally, multiple LRBM are superimposed, and the depth confidence network model based on LRBM is constructed and the performance of the depth network is analyzed. The experimental results show that the LRBM model can effectively extract the feature information of the data set,in the classification effect than the RBM average increase of about 2%,and improve the reliability of the target classification.关键词
受限玻尔兹曼机(RBM)/稀疏表示/特征提取/LRBM模型/目标分类Key words
Restricted Boltzmann Machine(RBM)/sparse representation/feature extraction/Lorentz function-based sparse constraints RBM(LRBM)/target classification分类
信息技术与安全科学引用本文复制引用
邹维宝,于昕玉,麦超..基于Lorentz函数的稀疏约束RBM模型的算法研究[J].计算机工程与应用,2018,54(7):213-220,231,9.
