Making matrices better: Geometry and topology of polar and singular value decomposition
For mathematicians and data scientists, this offers a conceptual framework for understanding matrix decompositions, but it is primarily expository rather than presenting new results.
This paper provides a geometric and topological perspective on the space of matrices, focusing on the structure of rank families and the roles of nearest orthogonal and singular neighbors in polar and singular value decompositions.
Our goal here is to see the space of matrices of a given size from a geometric and topological perspective, with emphasis on the families of various ranks and how they fit together. We pay special attention to the nearest orthogonal neighbor and nearest singular neighbor of a given matrix, both of which play central roles in matrix decompositions, and then against this visual backdrop examine the polar and singular value decompositions and some of their applications.