Subset kernel PCA for pattern recognition

@article{Washizawa2009SubsetKP,
  title={Subset kernel PCA for pattern recognition},
  author={Yoshikazu Washizawa},
  journal={2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops},
  year={2009},
  pages={162-169}
}
Subspace methods that utilize principal component analysis (PCA) are widely used for pattern classification or detection problems. Kernel PCA (KPCA) that is an extension of PCA is also applied to subspace methods. However, its computational cost is very high since the computational cost mainly depends on the number of samples in kernel methods. Recently, subset KPCA (SKPCA) has been proposed in order to reduce its computational complexity. In this paper, we apply SKPCA to subspace methods, and… CONTINUE READING