Generative and Discriminative Fuzzy Restricted Boltzmann Machine Learning for Text and Image Classification

doi:10.1109/TCYB.2018.2869902

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	Generative and Discriminative Fuzzy Restricted Boltzmann Machine Learning for Text and Image Classification
	Chen, C. L. Philip 1,2,3; Feng, Shuang 1,4
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
	2020-05-01
卷号	50 期号:5 页码:2237-2248
通讯作者	Feng, Shuang(fengshuang@bnuz.edu.cn)
摘要	The restricted Boltzmann machine (RBM) is an excellent generative learning model for feature extraction. By extending its parameters from real numbers to fuzzy ones, we have developed the fuzzy RBM (FRBM) which is demonstrated to possess better generative capability than RBM. In this paper, we first propose a generative model named Gaussian FRBM (GFRBM) to deal with real-valued inputs. Then, motivated by the fact that the discriminative variant of RBM can provide a self-contained framework for classification with competitive performance compared with some traditional classifiers, we establish the discriminative FRBM (DFRBM) and discriminative GFRBM (DGFRBM) that combine both the generative and discriminative facility by adding extra neurons next to the input units. Specifically, they can be trained into excellent stand-alone classifiers and retain outstanding generative capability simultaneously. The experimental results including text and image (both clean and noisy) classification indicate that DFRBM and DGFRBM outperform discriminative RBM models in terms of reconstruction and classification accuracy, and they behave more stable when encountering noisy data. Moreover, the proposed learning models show some promising advantages over other standard classifiers.
关键词	Data models Training Neurons Image reconstruction Feature extraction Computational modeling Cybernetics Discriminative learning fuzzy number Gaussian fuzzy restricted Boltzmann machine (GFRBM) image classification
DOI	10.1109/TCYB.2018.2869902
关键词[WOS]	POSSIBILISTIC MEAN-VALUE ; NEURAL-NETWORK ; RECOGNITION ; SYSTEMS ; LOGIC
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61751202] ; National Natural Science Foundation of China[61751205] ; National Natural Science Foundation of China[61572540] ; Macau Science and Technology Development Fund (FDCT)[019/2015/A1] ; Macau Science and Technology Development Fund (FDCT)[079/2017/A2] ; Macau Science and Technology Development Fund (FDCT)[024/2015/AMJ] ; MYRG of University of Macau ; Teacher Research Capacity Promotion Program of Beijing Normal University, Zhuhai
项目资助者	National Natural Science Foundation of China ; Macau Science and Technology Development Fund (FDCT) ; MYRG of University of Macau ; Teacher Research Capacity Promotion Program of Beijing Normal University, Zhuhai
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000528622000039
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：24[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/39360
专题	离退休人员
通讯作者	Feng, Shuang
作者单位	1.Univ Macau, Fac Sci & Technol, Macau 999078, Peoples R China 2.Dalian Maritime Univ, Dept Nav, Dalian 116026, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China 4.Beijing Normal Univ, Sch Appl Math, Zhuhai 519087, Peoples R China
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Chen, C. L. Philip,Feng, Shuang. Generative and Discriminative Fuzzy Restricted Boltzmann Machine Learning for Text and Image Classification[J]. IEEE TRANSACTIONS ON CYBERNETICS,2020,50(5):2237-2248.
APA	Chen, C. L. Philip,&Feng, Shuang.(2020).Generative and Discriminative Fuzzy Restricted Boltzmann Machine Learning for Text and Image Classification.IEEE TRANSACTIONS ON CYBERNETICS,50(5),2237-2248.
MLA	Chen, C. L. Philip,et al."Generative and Discriminative Fuzzy Restricted Boltzmann Machine Learning for Text and Image Classification".IEEE TRANSACTIONS ON CYBERNETICS 50.5(2020):2237-2248.