Brent Davis,  CSU Math

The MLV line for Pattern Recognition in High-Dimensional Data Sets

This talk will introduce an object to analyze a cluster of points embedded on a union of Grassmann manifolds, called the MLV line of best fit. One feature of the MLV line is the ability to capture common features of the points. The MLV line is a solution to an optimization problem involving a function of the principal angles between points. Two computational methods will be discussed including homotopy methods. We will apply this method to imaging data collected in the Pattern Analysis Lab. This is joint work with Dan Bates, Michael Kirby, Justin Marks, and Chris Peterson.