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Riemann

Riemann is an R package for learning with data on Riemannian manifolds. In statistics and machine learning, the term manifold appears in two realms; one is dimensionality reduction where we assume that low-dimensional data manifold is embedded in high-dimensional Euclidean space. The other is statistics on manifolds - data lie on some Riemannian manifolds that we are already well aware of. Riemann aims to achieve the latter. If you are interested in dimension reduction, please check another R package Rdimtools.

Installation

Option 1 : released version from CRAN.

install.packages("Riemann")

Option 2 : development version from GitHub.

if (!require("devtools")) {
  install.packages("devtools")
}
devtools::install_github("kisungyou/Riemann")

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Version

Install

install.packages('Riemann')

Monthly Downloads

251

Version

0.1.4

License

MIT + file LICENSE

Maintainer

Kisung You

Last Published

February 28th, 2022

Functions in Riemann (0.1.4)

grassmann.optmacg

Estimation of Distribution Algorithm with MACG Distribution

S3 method for mixture model : predict labels

Data : Gorilla Skull

grassmann.utest

Test of Uniformity on Grassmann Manifold

Angular Central Gaussian Distribution

grassmann.runif

Generate Uniform Samples on Grassmann Manifold

S3 method for mixture model : evaluate density

Data : EEG Covariances for Event-Related Potentials

Data : Left Hands

Data : Populated Cities in the U.S.

predict.m2skreg

Prediction for Manifold-to-Scalar Kernel Regression

Data : Normal Vectors to the Orbital Planes of the 9 Planets

Matrix Angular Central Gaussian Distribution

S3 method for mixture model : log-likelihood

Distance between Two Curves on Manifolds

Hierarchical Agglomerative Clustering

riem.coreset18B

Build Lightweight Coreset

Nonlinear Mean Shift

Geodesic Interpolation

Fr<U+00E9>chet Median and Variation

Data : Passiflora Leaves

K-Means++ Clustering

K-Means Clustering

K-Medoids Clustering

K-Means Clustering with Lightweight Coreset

Riemannian Manifold Metric Learning

Find K-Nearest Neighbors

Pairwise Distance on SPD Manifold

Supported Geometries on SPD Manifold

Generate Uniform Samples on Sphere

Principal Geodesic Analysis

Test of Uniformity on Sphere

Wrap Landmark Data on Shape Space

Competitive Learning Riemannian Quantization

Prepare Data on (Compact) Stiefel Manifold

wrap.multinomial

Prepare Data on Multinomial Manifold

Spectral Clustering by Shi and Malik (2000)

Kernel Principal Component Analysis

Spectral Clustering with Unnormalized Laplacian

Spectral Clustering by Ng, Jordan, and Weiss (2002)

Fr<U+00E9>chet Mean and Variation

Spectral Clustering by Zelnik-Manor and Perona (2005)

Multidimensional Scaling

Finite Mixture of Spherical Laplace Distributions

Simulated Annealing on Stiefel Manifold

Generate Uniform Samples on Stiefel Manifold

Prepare Data on SPD Manifold of Fixed-Rank

Prepare Data on Sphere

Wasserstein Barycenter of SPD Matrices

Dynamic Time Warping Distance

Prepare Data on Grassmann Manifold

Convert between Cartesian Coordinates and Geographic Coordinates

Prepare Data on Euclidean Space

Finite Mixture of Spherical Normal Distributions

Manifold-to-Scalar Kernel Regression with K-Fold Cross Validation

Geodesic Interpolation of Multiple Points

Manifold-to-Scalar Kernel Regression

Isometric Feature Mapping

Prepare Data on Rotation Group

Prepare Data on Symmetric Positive-Definite (SPD) Manifold

Test of Uniformity on Stiefel Manifold

riem.wasserstein

Wasserstein Distance between Empirical Measures

Generate Random Samples from Multivariate Normal Distribution

wrap.correlation

Prepare Data on Correlation Manifold

Compute Pairwise Distances for Data

Fr<U+00E9>chet Analysis of Variance

Compute Pairwise Distances for Two Sets of Data

Two-Sample Test modified from Biswas and Ghosh (2014)

Find the Smallest Enclosing Ball

Spherical Normal Distribution

Two-Sample Test with Wasserstein Metric

Spherical Laplace Distribution

t-distributed Stochastic Neighbor Embedding