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Data encoded as symmetric positive definite (SPD) matrices frequently arise in many areas of computer vision and machine learning.
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M. Schmidt, E. van den Berg, M. Friedlander, and K. Murphy, “Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm,” in International Conference on Artificial Intelligence and Statistics , 2009
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S. Jayasumana, R. Hartley, M. Salzmann, H. Li, and M. Harandi, “Kernel methods on riemannian manifolds with gaussian rbf kernels,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2015
2015
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A. Cichocki, S. Cruces, and S.-i. Amari, “Log-determinant divergences revisited: Alpha-beta and gamma log-det divergences,” Entropy , vol. 17, no. 5, pp. 2988–3034, 2015
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M. Harandi and M. Salzmann, “Riemannian coding and dictionary learning: Kernels to the rescue,” Computer Vision and Pattern Recognition , 2015
2015
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R. Sivalingam, D. Boley, V. Morellas, and N. Papanikolopoulos, “Tensor dictionary learning for positive definite matrices,” IEEE Transactions on Image Processing , vol. 24, no. 11, pp. 4592–4601, 2015
2015
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