Hash-SVM: Scalable Kernel Machines for Large-Scale Visual Classification

@article{Mu2014HashSVMSK,
  title={Hash-SVM: Scalable Kernel Machines for Large-Scale Visual Classification},
  author={Yadong Mu and Gang Hua and Wei Fan and Shih-Fu Chang},
  journal={2014 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2014},
  pages={979-986}
}
This paper presents a novel algorithm which uses compact hash bits to greatly improve the efficiency of non-linear kernel SVM in very large scale visual classification problems. Our key idea is to represent each sample with compact hash bits, over which an inner product is defined to serve as the surrogate of the original nonlinear kernels. Then the problem of solving the nonlinear SVM can be transformed into solving a linear SVM over the hash bits. The proposed Hash-SVM enjoys dramatic storage… CONTINUE READING
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