We study the problem of projecting high-dimensional tensor data on an unspecified Riemannian manifold onto some lower dimensional subspace1 without much distorting the pairwise geo...
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques: Principal Compo...
: Statistics and estimation theory is enriched with techniques derived from differential geometry. This establishes the increasing topic of information geometry. This allows new in...
Adaptive learning systems are recognized as one of the most interesting research topics in intelligent learning management systems. Taking as a point of departure the practices an...
Dimensionality reduction is a much-studied task in machine learning in which high-dimensional data is mapped, possibly via a non-linear transformation, onto a low-dimensional mani...