Hyperspectral Image Classification Based on Variational Relevance Vector Machine

Chunhui, 赵春晖 Zhao; Bin, 齐滨 Qi; Yi, 张燚 Zhang

doi:10.3788/aos201232.0828004

Cited by 3 publications

(2 citation statements)

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“…Furthermore, it does not have to satisfy the Mercer conditions and has higher model sparsity. And the model is capable of outputting probability distribution with prediction results, and widely applied to engineering fields such as sewage detection (Zeng, T., 2013), hyperspectral image classification (Demir, B., 2007;Zhao, C., 2012;Yang, G., 2010), fault diagnosis and prognostics (Huang, K., 2010;Moghanjooghi, H. A., 2012) and so on nowadays.…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Diagnosis Method Using Improved Multi-Class Classification Algorithm and Relevance Vector Machine

Kang

Chi

2015

International Journal of Information Technology and Web Engineering

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In view of the problems in traditional fault diagnosis method, such as small samples and nonlinear relations, a fault diagnosis method based on improved multi-class classification algorithm and relevance vector machine (RVM) is proposed in the paper. Through improving the majority-vote strategy of traditional One-Against-One (OAO) algorithm and combining the features of OAO and One-Against-Rest (OAR) algorithms, the k-class classification problem is transformed into k(k-1)/2 three-class classification problems based on the proposed majority-vote strategy of double-layer and thereby an improved multi-class classification algorithm of One-Against-One-Against-Rest (OAOAR) is presented. And on each three-class classification issue, OAO and RVM as the binary classifier are adopted to achieve the multi-class classification of RVM. Numerical simulations of UCI datasets and fault diagnostic experiments results of power transformers both demonstrate that the proposed method performs significantly better than other traditional methods in terms of increasing the diagnostic accuracy, optimizing the voting results, strengthening the diagnostic confidence and identifying the hidden classes, and has more practical value in engineering.

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Section: Introductionmentioning

confidence: 99%

Research on Fault Diagnosis Method Using Improved Multi-Class Classification Algorithm and Relevance Vector Machine

Kang

Chi

2015

International Journal of Information Technology and Web Engineering

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“…The assignment of an individual hyper-parameter to each weight is the ultimate reason for the sparse property of RVM. For more information about RVM see reference [14]， [17][18].…”

Section: Relevance Vector Machine Classier Introductionmentioning

confidence: 99%

RVM Classification of Hyperspectral Images Based on Wavelet Kernel Non-negative Matrix Fractorization

Bai

Hui

et al. 2015

TELKOMNIKA

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Hyperspectral Image Classification Based on Composite Kernel Relevance Vector Machine

孙

2023

CSA

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Hyperspectral Image Classification Based on Variational Relevance Vector Machine

Cited by 3 publications

References 0 publications

Research on Fault Diagnosis Method Using Improved Multi-Class Classification Algorithm and Relevance Vector Machine

Research on Fault Diagnosis Method Using Improved Multi-Class Classification Algorithm and Relevance Vector Machine

RVM Classification of Hyperspectral Images Based on Wavelet Kernel Non-negative Matrix Fractorization

Hyperspectral Image Classification Based on Composite Kernel Relevance Vector Machine

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