The next Foundations of Data Science virtual talk will take place on Thursday, April 1st at 10:00 AM Pacific Time (13:00 Eastern Time, 19:00 Central European Time, 17:00 UTC). Ingrid Daubechies from Duke Univeristy will speak about “Discovering low-dimensional manifolds in high-dimensional data sets.”
Abstract: This talk reviews diffusion methods to identify low-dimensional manifolds underlying high-dimensional datasets, and illustrates that by pinpointing additional mathematical structure, improved results can be obtained. Much of the talk draws on a case study from a collaboration with biological morphologists, who compare different phenotypical structures to study relationships of living or extinct animals with their surroundings and each other. This is typically done from carefully defined anatomical correspondence points (landmarks) on e.g. bones; such landmarking draws on highly specialized knowledge. To make possible more extensive use of large (and growing) databases, algorithms are required for automatic morphological correspondence maps, without any preliminary marking of special features or landmarks by the user.
The series is supported by the NSF HDR TRIPODS Grant 1934846.