CASIA OpenIR
From Class-Specific to Class-Mixture: Cascaded Feature Representations via Restricted Boltzmann Machine Learning
Xie, Guo-Sen1,2; Jin, Xiao-Bo3; Zhang, Xu-Yao4; Zang, Shao-Fei1,2; Yang, Chunlei1,2; Wang, Zhiheng5; Pu, Jiexin1,2
Source PublicationIEEE ACCESS
ISSN2169-3536
2018
Volume6Pages:69393-69406
Corresponding AuthorXie, Guo-Sen(gsxiehm@gmail.com)
AbstractIn this paper, we propose two kinds of feature extracting frameworks that can extract cascaded class-specific and class-mixture features, respectively, by taking the restricted Boltzmann machine (RBM) as the basic building blocks; we further call them as a CS-RBM and CM-RBM feature extractor. The discriminations of features from both CS-RBM and CM-RBM are verified better than the class-independent (traditional) RBM (CI-RBM) feature extractor. As one mini-batch samples are randomly selected from all classes during the training phase of the traditional RBM, which can make that the above mini-batch data contain easy-confusing samples from different categories. Therefore, the features from CI-RBM are difficult to distinguish these samples from the confused categories. CS-RBM and CM-RBM can overcome the above sample confusing problem efficiently and effectively. To cope with the real-valued input samples, we further extend the binary RBM to Gaussian-Bernoulli RBM (GBRBM), leading to the CS-GBRBM (CM-GBRBM) feature extracting framework. Experiments on binary datasets, i.e., MNIST and USPS, scene image dataset (Scene-15), and object image dataset (Coil-100), well verify the above facts and show the competitive results.
KeywordFeature extraction class-specific RBM feature learning
DOI10.1109/ACCESS.2018.2878553
WOS KeywordCLASSIFICATION ; RECOGNITION ; MODEL
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61702163] ; National Natural Science Foundation of China[61103138] ; National Natural Science Foundation of China[U1804326] ; Henan University Scientific and Technological Innovation Team Support Program[19IRTSTHN012] ; Fundamental Research Funds for the Henan Provincial Colleges and Universities in the Henan University of Technology[2016RCJH06]
Funding OrganizationNational Natural Science Foundation of China ; Henan University Scientific and Technological Innovation Team Support Program ; Fundamental Research Funds for the Henan Provincial Colleges and Universities in the Henan University of Technology
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000452598800001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25672
Collection中国科学院自动化研究所
Corresponding AuthorXie, Guo-Sen
Affiliation1.Henan Univ Sci & Technol, Dept Informat, Engn Coll, Luoyang 471023, Peoples R China
2.Henan Univ Sci & Technol, Henan Joint Int Res Lab Image Proc & Intelligent, Luoyang 471023, Peoples R China
3.Henan Univ Technol, Sch Informat Sci & Engn, Zhengzhou 450001, Henan, Peoples R China
4.Chinese Acad Sci, NLPR, Inst Automat, Beijing 100190, Peoples R China
5.Henan Polytech Univ, Dept Comp Sci & Technol, Jiaozuo 454003, Peoples R China
Recommended Citation
GB/T 7714
Xie, Guo-Sen,Jin, Xiao-Bo,Zhang, Xu-Yao,et al. From Class-Specific to Class-Mixture: Cascaded Feature Representations via Restricted Boltzmann Machine Learning[J]. IEEE ACCESS,2018,6:69393-69406.
APA Xie, Guo-Sen.,Jin, Xiao-Bo.,Zhang, Xu-Yao.,Zang, Shao-Fei.,Yang, Chunlei.,...&Pu, Jiexin.(2018).From Class-Specific to Class-Mixture: Cascaded Feature Representations via Restricted Boltzmann Machine Learning.IEEE ACCESS,6,69393-69406.
MLA Xie, Guo-Sen,et al."From Class-Specific to Class-Mixture: Cascaded Feature Representations via Restricted Boltzmann Machine Learning".IEEE ACCESS 6(2018):69393-69406.
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