Please join us for the Statistics Seminar scheduled from 11 a.m. to noon on Friday, Feb. 16.
Our distinguished guest speaker, Maryam Bagherian from the University of Massachusetts, Boston, will be presenting her research on "Distance Metric Learning and Its Application."
The seminar will be conducted in a hybrid format, allowing both in-person (HOS 380) and virtual attendance. Further details can be found below.
Abstract:
Distance metric learning serves as an approach for uncovering hidden structures within high-dimensional spaces. Through the acquisition of a suitable distance metric, algorithms reliant on distance measurements can more effectively capture the inherent structure of data points, resulting in enhanced performance. In contrast to single metric learning methods, the effectiveness of multi-metric and geometric metric learning becomes evident in handling intricate data distributions and diverse data characteristics. These alternative approaches offer heightened flexibility and interpretability, making them especially valuable for representation learning in intricate non-linear multi-modal datasets. In this context, I provide a concise introduction to the concepts of distance metric learning and introduce methods for extending its applicability to high-dimensional spaces, graphs and manifolds.
- Zoom Link
- Meeting ID: 915 6962 6472
- Passcode: 47773444
