Review of research on restricted Boltzmann machine and its variants

被引：0
|
作者：
Wang Q. [1 ]
Gao X. [1 ]
Wu B. [2 ]
Hu Z. [1 ]
Wan K. [1 ]
机构：
[1] School of Electronic Information, Northwestern Polytechnical University, Xi'an
[2] Stratégie et Économie d'Entreprise, Paris 1 Panthéon Sorbonne, Paris
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2024年 / 46卷 / 07期
关键词：
deep learning; feature extraction; probabilistic undirected graph; restricted Boltzmann machine (RBM); restricted Boltzmann machine variants;
D O I：
10.12305/j.issn.1001-506X.2024.07.16
中图分类号：
学科分类号：
摘要：
As a typical probabilistic graphical model for learning data distribution and extracting intrinsic features, the restricted Boltzmann machine (RBM) is an important fundamental model in the field of deep learning. In recent years, numerous emerging models, i. e., RBM variants, have been obtained by improving the model structure and energy function of RBM, which can further enhance the feature extraction performance of the model. The study of RBM and its variants can significantly contribute to the development of the deep learning field and realize the rapid extraction of massive information in the era of big data. Based on this, the relevant research on RBM and its variants are systematically reviewed in recent years, and the improvement of training algorithm, model structure, deep model fusion research and the latest application are creatively reviewed. In particular, the focus is on sorting out the develop history of training algorithms and variants for RBM. Finally, the existing difficulties and challenges in the field of RBM and its variants are discussed, and the main research work is summarized and prospected. © 2024 Chinese Institute of Electronics. All rights reserved.
引用
收藏
页码：2323 / 2345
页数：22
相关论文
共 127 条
  • [91] HAN Z Z, LIU Z B, HAN J W, Et al., Mesh convolutional restricted Boltzmann machines for unsupervised learning of features with structure preservation on 3-D meshes, IEEE Trans. on Neural Networks and Learning Systems, 28, 10, pp. 2268-2281, (2017)
  • [92] KARTHIK E, SETHUKARASI T., A centered convolutional restricted Boltzmann machine optimized by hybrid atom search arithmetic optimization algorithm for sentimental analysis, Neural Processing Letters, 54, 5, pp. 4123-4151, (2022)
  • [93] KANNO Y, YASUDA M., Multi-layered discriminative restricted Boltzmann machine with untrained probabilistic layer, Proc. of the International Conference on Pattern Recognition, pp. 7655-7660, (2021)
  • [94] VAN-TULDER G, DE-BRUIJNE M., Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted Boltzmann machines, IEEE Trans. on Medical Imaging, 35, 5, pp. 1262-1272, (2016)
  • [95] CHEN C L P, FENG S., Generative and discriminative fuzzy restricted Boltzmann machine learning for text and image classification, IEEE Trans. on Cybernetics, 50, 5, pp. 2237-2248, (2020)
  • [96] LUO L K, ZHANG S F, WANG Y D, Et al., An alternate method between generative objective and discriminative objective in training classification restricted Boltzmann machine, Knowledge Based Systems, 144, pp. 144-152, (2018)
  • [97] XU A Q, TIAN M W, FIROUZI B, Et al., A new deep learning restricted Boltzmann machine for energy consumption forecasting, Sustainability-Basel, 14, 16, (2022)
  • [98] JUSTIN S, SALEH W, AL GHAMDI T, Et al., Hyper parameter optimization based deep belief network for clean buses using solar energy model, Intelligent Automation and Soft Computing, 37, 1, pp. 1091-1109, (2023)
  • [99] WANG Q, YANG X L, PU D, Et al., Sustainable investment forecasting of power grids based on the deep restricted Boltzmann machine optimized by the lion algorithm, Computer Modeling in Engineering and Sciences, 130, 1, pp. 269-286, (2022)
  • [100] SHIN U, ISHH M, OKAZAKI A, Et al., Pattern training, inference, and regeneration demonstration using on-chip trainable neuromorphic chips for spiking restricted Boltzmann machine, Advance Intelligent Systems, 4, 8, (2022)
45678910111213