RiemGrassmann v0.1.0

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Inference, Learning, and Optimization on Grassmann Manifold

Grassmann manifold is a set of k-planes or linear subspaces in Euclidean space. We provide algorithms for statistical inference, optimization, and learning over the Grassmann manifold. For general exposition to the statistics on the manifold, see the book by Chikuse (2003) <doi:10.1007/978-0-387-21540-2>.

Readme

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

Functions in RiemGrassmann

Name Description
package-RiemGrassmann Inference, Learning, and Optimization on Grassmann Manifold
gr.pdist2 Pairwise Distance for Two Sets Data on Grassmann Manifold
gr.kmedoids k-Medoids Clustering on Grassmann Manifold
gr.pdist Pairwise Distance for Data on Grassmann Manifold
gr.mean Fr<U+00E9>chet Mean on Grassmann Manifold
gr.hclust Hierarchical Agglomerative Clustering on Grassmann Manifold
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Details

Type Package
Encoding UTF-8
License GPL (>= 3)
LinkingTo Rcpp, RcppArmadillo, RiemBase
RoxygenNote 7.0.2
NeedsCompilation yes
Packaged 2020-03-25 15:26:03 UTC; kisung
Repository CRAN
Date/Publication 2020-03-25 15:50:06 UTC

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