Speaker: Dr. Maryam Bagherian

Title: Distance Metric Learning and its application in high dimensional data analysis

Abstract: Distance metric learning is an approach in high-dimensional analysis which
facilitates the exploration of underlying structures among data points in an effective and
efficient manner. By learning a suitable distance metric, distance-based algorithms can better
capture the intrinsic structure of data points, leading to improved performance in various
tasks.

Multi-metric learning and geometric metric learning, in contrast to single-metric learning
approaches, have demonstrated higher efficiency in handling complex data distributions or
diverse data characteristics. These approaches offer increased flexibility and interpretability
and are particularly helpful in representation learning for complex multi-modal datasets.

In this context, we provide a brief introduction to the concepts of metric learning mentioned
above and outline the algorithms that we have developed under metric learning constraints.
Furthermore, we highlight examples of where these approaches can be applied to enhance
performance and interpretability in various machine learning tasks.