A new metric on the manifold of kernel matrices with application to matrix geometric means

Symmetric positive definite (spd) matrices are remarkably pervasive in a multitude of scientific disciplines, including machine learning and optimization. We consider the fundamental task of measuring distances between two spd matrices; a task that is often nontrivial whenever an application demands the distance function to respect the non-Euclidean geometry of spd matrices... (read more)

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