Issam Moindji茅 (UQAM)
Title:聽A functional data approach for statistical shapes analysis
Abstract:
The shape聽X~X~聽of a random planar curve,聽XX, is what remains when the deformation variables (scaling, rotation, translation, and reparametrization) are removed. Previous studies in statistical shape analysis have focused on analyzing聽X~X~聽through discrete observations of聽XX. While this approach has some computational advantages, it overlooks the continuous nature of variables:聽X~X~,聽XX, and it ignores the potential dependence of deformation variables on each other and聽X~X~, which results in a loss of information in the data structure. I will introduce a new framework for studying聽XX聽based on functional data analysis in this presentation. Basis expansion techniques are employed to find analytic solutions for deformation variables such as rotation and parametrization deformations. Then, the generative model of聽XX聽is investigated using a joint-principal component analysis approach. Numerical experiments on synthetic and real datasets demonstrate how this new approach performs better at analyzing random planar curves than traditional functional data methods.
Speaker
Issam Moindji茅 obtained his PhD in Statistics in 2023 from the University of Lille, France. He has been a postdoctoral researcher at UQAM since June 2024. His current research interests include functional data analysis, statistical shape analysis, and spatial statistics.
Meeting ID: 878 2435 7176
Passcode: None