Given repeated observations of several subjects over time, ie a longitudinal data set, this
paper introduces a new model to learn a classification of the shapes progression in an …

In many applications, geodesic hierarchical models are adequate for the study of temporal
observations. We employ such a model derived for manifold-valued data to Kendall's shape …

Even though the number of longitudinal resting-state-fMRI studies is increasing, accurately
characterizing the changes in functional connectivity across visits is a largely unexplored …

For decades, de Casteljau's algorithm has been used as a fundamental building block in
curve and surface design and has found a wide range of applications in fields such as …

Statistical analysis of longitudinal data is a significant problem in Biomedical imaging
applications. In the recent past, several researchers have developed mathematically …

Data sets sampled in Lie groups are widespread, and as with multivariate data, it is
important for many applications to assess the differences between the sets in terms of their …

Analyzing data representing multifarious trajectories is central to the many fields in Science
and Engineering; for example, trajectories representing a tennis serve, a gymnast's parallel …

We propose generalizations of the Hotelling's T^ 2 statistic and the Bhattacharayya distance
for data taking values in Lie groups. A key feature of the derived measures is that they are …

Recent results in coupled or temporal graphical models offer schemes for estimating the
relationship structure between features when the data come from related (but distinct) …

Modern machine learning has proven to be extremely effective in aiding and automating a
large number of tasks, beginning with simple image recognition, now ranging widely from …