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A Novel Restricted Boltzmann Machine Training Algorithm with Fast Gibbs Sampling Policy
Joint Authors
Gao, Xiaoguang
Wang, Qianglong
Wan, Kaifang
Li, Fei
Hu, Zijian
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-19, 19 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-03-20
Country of Publication
Egypt
No. of Pages
19
Main Subjects
Abstract EN
The restricted Boltzmann machine (RBM) is one of the widely used basic models in the field of deep learning.
Although many indexes are available for evaluating the advantages of RBM training algorithms, the classification accuracy is the most convincing index that can most effectively reflect its advantages.
RBM training algorithms are sampling algorithms essentially based on Gibbs sampling.
Studies focused on algorithmic improvements have mainly faced challenges in improving the classification accuracy of the RBM training algorithms.
To address the above problem, in this paper, we propose a fast Gibbs sampling (FGS) algorithm to learn the RBM by adding accelerated weights and adjustment coefficient.
An important link based on Gibbs sampling theory was established between the update of the network weights and mixing rate of Gibbs sampling chain.
The proposed FGS method was used to accelerate the mixing rate of Gibbs sampling chain by adding accelerated weights and adjustment coefficients.
To further validate the FGS method, numerous experiments were performed to facilitate comparisons with the classical RBM algorithm.
The experiments involved learning the RBM based on standard data.
The results showed that the proposed FGS method outperformed the CD, PCD, PT5, PT10, and DGS algorithms, particularly with respect to the handwriting database.
The findings of our study suggest the potential applications of FGS to real-world problems and demonstrate that the proposed method can build an improved RBM for classification.
American Psychological Association (APA)
Wang, Qianglong& Gao, Xiaoguang& Wan, Kaifang& Li, Fei& Hu, Zijian. 2020. A Novel Restricted Boltzmann Machine Training Algorithm with Fast Gibbs Sampling Policy. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-19.
https://search.emarefa.net/detail/BIM-1195026
Modern Language Association (MLA)
Wang, Qianglong…[et al.]. A Novel Restricted Boltzmann Machine Training Algorithm with Fast Gibbs Sampling Policy. Mathematical Problems in Engineering No. 2020 (2020), pp.1-19.
https://search.emarefa.net/detail/BIM-1195026
American Medical Association (AMA)
Wang, Qianglong& Gao, Xiaoguang& Wan, Kaifang& Li, Fei& Hu, Zijian. A Novel Restricted Boltzmann Machine Training Algorithm with Fast Gibbs Sampling Policy. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-19.
https://search.emarefa.net/detail/BIM-1195026
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195026