Riemannian kernel based Nyström method for approximate infinite-dimensional covariance descriptors with application to image set classification

@article{Chen2018RiemannianKB,
  title={Riemannian kernel based Nystr{\"o}m method for approximate infinite-dimensional covariance descriptors with application to image set classification},
  author={Kai Chen and Xiaojun Wu and Rui Wang and Josef Kittler},
  journal={2018 24th International Conference on Pattern Recognition (ICPR)},
  year={2018},
  pages={651-656}
}
In the domain of pattern recognition, using the CovDs (Covariance Descriptors) to represent data and taking the metrics of the resulting Riemannian manifold into account have been widely adopted for the task of image set classification. Recently, it has been proven that infinite-dimensional CovDs are more discriminative than their low-dimensional counterparts. However, the form of infinite-dimensional CovDs is implicit and the computational load is high. We propose a novel framework for… Expand
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