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

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 evaluated 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.

Key words: Restricted Boltzmann Machine（RBM）, sparse representation, feature extraction, Lorentz function-based sparse constraints RBM（LRBM）, target classification