UM
Nonparametric Bayesian Correlated Group Regression With Applications to Image Classification
Luo, Lei1,2; Yang, Jian3; Zhang, Bob4; Jiang, Jielin5; Huang, Heng2
2018-11
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
Volume29Issue:11Pages:5330-5344
AbstractSparse Bayesian learning has emerged as a powerful tool to tackle various image classification tasks. The existing sparse Bayesian models usually use independent Gaussian distribution as the prior knowledge for the noise. However, this assumption often contradicts to the practical observations in which the noise is long tail and pixels containing noise are spatially correlated. To handle the practical noise, this paper proposes to partition the noise image into several 2-D groups and adopt the long-tail distribution, i.e., the scale mixture of the matrix Gaussian distribution, to model each group to capture the intragroup correlation of the noise. Under the nonparametric Bayesian estimation, the low-rank-induced prior and the matrix Gamma distribution prior are imposed on the covariance matrix of each group, respectively, to induce two Bayesian correlated group regression (BCGR) methods. Moreover, the proposed methods are extended to the case with unknown group structure. Our BCGR method provides an effective way to automatically fit the noise distribution and integrates the long-tail attribute and structure information of the practical noise into model. Therefore, the estimated coefficients are better for reconstructing the desired data. We apply BCGR to address image classification task and utilize the learned covariance matrices to construct a grouped Mahalanobis distance to measure the reconstruction residual of each class in the design of a classifier. Experimental results demonstrate the effectiveness of our new BCGR model.
KeywordCorrelated group regression expectation-maximization (EM) robust image classification scale mixture of matrix Gaussian distribution sparse Bayesian learning (SBL)
DOI10.1109/TNNLS.2018.2797539
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000447832200013
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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Cited Times [WOS]:7   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Jiangsu Key Lab Image & Video Understanding Socia, Nanjing 210094, Jiangsu, Peoples R China;
2.Univ Pittsburgh, Sch Elect & Comp Engn, Pittsburgh, PA 15261 USA;
3.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Minist Educ, Key Lab Intelligent Percept & Syst High Dimens In, Nanjing 210094, Jiangsu, Peoples R China;
4.Univ Macau, Dept Comp & Informat Sci, Taipa 999078, Macao, Peoples R China;
5.Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Luo, Lei,Yang, Jian,Zhang, Bob,et al. Nonparametric Bayesian Correlated Group Regression With Applications to Image Classification[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(11):5330-5344.
APA Luo, Lei,Yang, Jian,Zhang, Bob,Jiang, Jielin,&Huang, Heng.(2018).Nonparametric Bayesian Correlated Group Regression With Applications to Image Classification.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(11),5330-5344.
MLA Luo, Lei,et al."Nonparametric Bayesian Correlated Group Regression With Applications to Image Classification".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.11(2018):5330-5344.
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