Hyperspectral Image Classification Using Stochastic Gradient Descent Based Support Vector Machine

2020 ◽  
pp. 78-84
Author(s):  
Pattem Sampurnima ◽  
Sandeep Kumar Satapathy ◽  
Shruti Mishra ◽  
Pradeep Kumar Mallick
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Gradient Descent ◽  
Hyperspectral Image ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Hyperspectral Image Classification

scholarly journals Stochastic Subgradient for Large-Scale Support Vector Machine Using the Generalized Pinball Loss Function

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1652
Author(s):  
Wanida Panup ◽  
Rabian Wangkeeree
Keyword(s):  
Support Vector Machine ◽  
Loss Function ◽  
Gradient Descent ◽  
Large Scale ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Hinge Loss ◽  
Gradient Descent Algorithm ◽  
Pinball Loss

In this paper, we propose a stochastic gradient descent algorithm, called stochastic gradient descent method-based generalized pinball support vector machine (SG-GPSVM), to solve data classification problems. This approach was developed by replacing the hinge loss function in the conventional support vector machine (SVM) with a generalized pinball loss function. We show that SG-GPSVM is convergent and that it approximates the conventional generalized pinball support vector machine (GPSVM). Further, the symmetric kernel method was adopted to evaluate the performance of SG-GPSVM as a nonlinear classifier. Our suggested algorithm surpasses existing methods in terms of noise insensitivity, resampling stability, and accuracy for large-scale data scenarios, according to the experimental results.

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