Inference, Learning, and Optimization on Grassmann manifold

Grassmannian is a set of linear subspaces, which forms a Riemannian manifold. We provide algorithms for statistical inference, optimization, and learning over the Grassmann manifold.

Installation

You can install the released version of RiemGrassmann from CRAN with:

install.packages("RiemGrassmann")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("kyoustat/RiemGrassmann")

Available Functions

function	description
`gr.hclust`	Hierarchical clustering.
`gr.kmedoids`	k-Medoids clustering.
`gr.mean`	Frechet mean and variation.
`gr.pdist`	Pairwise distance for Grassmann-valued data
`gr.pdist2`	Pairwise distance between two sets of data