Identifying Informative Distance Measures in High-Dimensional Feature Spaces

This seminar features Prof. Alessandro Laio from the International School for Advanced Studies in Trieste, Italy, discussing methods for identifying informative distance measures in high-dimensional feature spaces. Learn how to assess similarity between data points containing heterogeneous features with different relevance and units of measure. Discover a novel approach that evaluates the relative information retained by different distance measures, determining if they are equivalent, independent, or if one is more informative than another. Understand how this methodology can be applied to identify optimal collective variables in molecular systems and infer causality in high-dimensional dynamic processes and time series. The 47-minute Lennard-Jones Centre discussion group seminar was held on January 29th, 2024, and is presented by Cambridge Materials.