Graph-regularized Bayesian broad learning system

Junwei DUAN; Lincan XU; Yujuan QUAN; Long CHEN; C.L.Philip CHEN

doi:10.11959/j.issn.2096-6652.202203

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Graph-regularized Bayesian broad learning system

Papers and Reports | 更新时间：2024-06-05

- Graph-regularized Bayesian broad learning system
- Chinese Journal of Intelligent Science and Technology Vol. 4, Issue 1, Pages: 109-117(2022)
- 作者机构：
  
  1. 暨南大学信息科学技术学院，广东广州 510632
  2. 澳门大学科技学院，澳门 999078
  3. 华南理工大学计算机科学与工程学院，广东广州 510006
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202203
  CLC： TP181
- Published Online：2022-03，
  
  Published：15 March 2022
- 稿件说明：
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Junwei DUAN, Lincan XU, Yujuan QUAN, et al. Graph-regularized Bayesian broad learning system[J]. Chinese journal of intelligent science and technology, 2022, 4(1): 109-117.
DOI：

Junwei DUAN, Lincan XU, Yujuan QUAN, et al. Graph-regularized Bayesian broad learning system[J]. Chinese journal of intelligent science and technology, 2022, 4(1): 109-117. DOI： 10.11959/j.issn.2096-6652.202203.

摘要

作为一种前馈神经网络，宽度学习系统因其精度高、训练速度快且能有效代替深度学习方法而备受研究者的关注。然而，宽度学习系统存在对网络中的特征节点个数比较敏感且求伪逆方式易使模型出现过拟合等问题。为此，在宽度学习系统中引入贝叶斯推断和图正则化。一方面，通过引入先验知识进行贝叶斯学习可以有效提高权重的稀疏性，提高模型的稳定性；另一方面，加入图正则化可充分考虑数据内在的图信息，进一步提高模型的泛化能力。在UCI数据集和NORB数据集上对所提模型进行性能评估，实验结果表明，所提的基于图正则化的贝叶斯宽度学习系统模型能进一步提高宽度学习系统的分类精度且具有更好的稳定性。

Abstract

As a feed forward neural network

broad learning system (BLS) has attracted much attention because of its high accuracy

fast training speed

and the ability to effectively replace deep learning methods.However

it is sensitive to the number of feature nodes and the pseudo-inverse method is likely to result in the problem of over fitting for BLS model.To address the above issues

Bayesian inference and graph regularization was introduced in to the BLS model.By introducing the prior knowledge for Bayesian learning

the sparsity of the weights and the stability of the model could be effectively improved; while the graph information mining from the data could be fully considered to improve the generalization ability of the model by regularization.The UCI and NORB dataset were adopted for evaluating the performance of the proposed model.The experiment results demonstrated that the proposed graph-regularized Bayesian broad learning system model can further improve the accuracy of classification and has better stability.

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references

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