Positive definite matrices and the Symmetric Stein Divergence

By Suvrit Sra. 2011

68 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b85f31235a3cbd70d082e8bbb81dc6fedcbc6841

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Mehrtash Harandi
2. Mathieu Salzmann
3. G. Peyr'e
4. Marco Cuturi
5. Zhiwu Huang
6. Brian C. Lovell
7. Xikang Zhang
8. Justin M. Solomon

Citations in the corpus

• Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold (2016, background): “Although JBLD itself is not a metric, Sra [34] proved that its square root δld is.”
• Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices (2013, background): “This, for instance, was shown in [18] for the root Stein divergence metric.”
• Gromov-Wasserstein Averaging of Kernel and Distance Matrices (2016, background): “(Sra, 2011) defines a metric on registered positive definite kernel matrices that can be used for computing Fréchet means.”
• Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach (2012, methodology): “Interested readers can follow the proof in [17].”