Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices

@article{Cherian2017RiemannianDL,
  title={Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices},
  author={A. Cherian and S. Sra},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2017},
  volume={28},
  pages={2859-2871}
}
  • A. Cherian, S. Sra
  • Published 2017
  • Computer Science, Medicine
  • IEEE Transactions on Neural Networks and Learning Systems
  • Data encoded as symmetric positive definite (SPD) matrices frequently arise in many areas of computer vision and machine learning. While these matrices form an open subset of the Euclidean space of symmetric matrices, viewing them through the lens of non-Euclidean Riemannian (Riem) geometry often turns out to be better suited in capturing several desirable data properties. Inspired by the great success of dictionary learning and sparse coding (DLSC) for vector-valued data, our goal in this… CONTINUE READING
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