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TV-SVM: Support Vector Machine with Total Variational Regularization

Citation Author(s):
Zhendong Zhang,Cheolkon Jung
Submitted by:
Zhendong Zhang
Last updated:
13 April 2018 - 12:30pm
Document Type:
Poster
Document Year:
2018
Event:
Presenters:
Zhendong Zhang
Paper Code:
1082
 

To leverage the spatial relationship of lattice data, such as images, we introduce total variational (TV) regularization into support vector machines (SVM), called TV-SVM. TV-SVM encourages local smoothness and sparsity in gradient domain of the learned parameters. TV-SVM is optimized via the alternating direction method of multipliers (ADMM) algorithm and is significantly better than (Linear) SVM for image classifications.

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