Search Results for author:
Found 3 papers, 1 papers with code
Persistent Homology on Grassmann Manifolds for Analysis of Hyperspectral Movies
This realization has motivated the development of new tools such as persistent homology for exploring topological invariants, or features, in large data sets.
Persistence Images: A Stable Vector Representation of Persistent Homology
We convert a PD to a finite-dimensional vector representation which we call a persistence image (PI), and prove the stability of this transformation with respect to small perturbations in the inputs.
Ranked #4 on
Graph Classification
on NEURON-BINARY
Classification of Hyperspectral Imagery on Embedded Grassmannians
The resulting points on the Grassmannian have representations as orthonormal matrices and as such do not reside in Euclidean space in the usual sense.
