Learning with Symmetric Positive Definite Matrices via Generalized Bures-Wasserstein Geometry

Explore the advanced mathematical concept of learning with Symmetric Positive Definite Matrices through the lens of Generalized Bures Wasserstein Geometry in this 20-minute conference talk presented at GSI. Delve into the intricate relationships between matrix theory, geometry, and machine learning as the speaker unravels the complexities of this specialized topic. Gain insights into how this approach can enhance understanding and applications in fields such as data analysis, computer vision, and signal processing.

Learning with Symmetric Positive Definite Matrices via Generalized Bures Wasserstein Geometry