Man pages for Riemann
Learning with Data on Riemannian Manifolds
| acg | Angular Central Gaussian Distribution |
| cities | Data : Populated Cities in the U.S. |
| density | S3 method for mixture model : evaluate density |
| ERP | Data : EEG Covariances for Event-Related Potentials |
| gorilla | Data : Gorilla Skull |
| grassmann.optmacg | Estimation of Distribution Algorithm with MACG Distribution |
| grassmann.runif | Generate Uniform Samples on Grassmann Manifold |
| grassmann.utest | Test of Uniformity on Grassmann Manifold |
| hands | Data : Left Hands |
| label | S3 method for mixture model : predict labels |
| loglkd | S3 method for mixture model : log-likelihood |
| macg | Matrix Angular Central Gaussian Distribution |
| moSL | Finite Mixture of Spherical Laplace Distributions |
| moSN | Finite Mixture of Spherical Normal Distributions |
| orbital | Data : Normal Vectors to the Orbital Planes of the 9 Planets |
| passiflora | Data : Passiflora Leaves |
| predict.m2skreg | Prediction for Manifold-to-Scalar Kernel Regression |
| riem.clrq | Competitive Learning Riemannian Quantization |
| riem.coreset18B | Build Lightweight Coreset |
| riem.distlp | Distance between Two Curves on Manifolds |
| riem.dtw | Dynamic Time Warping Distance |
| riem.fanova | Fréchet Analysis of Variance |
| riem.hclust | Hierarchical Agglomerative Clustering |
| riem.interp | Geodesic Interpolation |
| riem.interps | Geodesic Interpolation of Multiple Points |
| riem.isomap | Isometric Feature Mapping |
| riem.kmeans | K-Means Clustering |
| riem.kmeans18B | K-Means Clustering with Lightweight Coreset |
| riem.kmeanspp | K-Means++ Clustering |
| riem.kmedoids | K-Medoids Clustering |
| riem.knn | Find K-Nearest Neighbors |
| riem.kpca | Kernel Principal Component Analysis |
| riem.m2skreg | Manifold-to-Scalar Kernel Regression |
| riem.m2skregCV | Manifold-to-Scalar Kernel Regression with K-Fold Cross... |
| riem.mds | Multidimensional Scaling |
| riem.mean | Fréchet Mean and Variation |
| riem.median | Fréchet Median and Variation |
| riem.nmshift | Nonlinear Mean Shift |
| riem.pdist | Compute Pairwise Distances for Data |
| riem.pdist2 | Compute Pairwise Distances for Two Sets of Data |
| riem.pga | Principal Geodesic Analysis |
| riem.phate | PHATE |
| riem.rmml | Riemannian Manifold Metric Learning |
| riem.sammon | Sammon Mapping |
| riem.sc05Z | Spectral Clustering by Zelnik-Manor and Perona (2005) |
| riem.scNJW | Spectral Clustering by Ng, Jordan, and Weiss (2002) |
| riem.scSM | Spectral Clustering by Shi and Malik (2000) |
| riem.scUL | Spectral Clustering with Unnormalized Laplacian |
| riem.seb | Find the Smallest Enclosing Ball |
| riem.test2bg14 | Two-Sample Test modified from Biswas and Ghosh (2014) |
| riem.test2wass | Two-Sample Test with Wasserstein Metric |
| riem.tsne | t-distributed Stochastic Neighbor Embedding |
| riem.wasserstein | Wasserstein Distance between Empirical Measures |
| rmvnorm | Generate Random Samples from Multivariate Normal Distribution |
| spd.geometry | Supported Geometries on SPD Manifold |
| spd.pdist | Pairwise Distance on SPD Manifold |
| spd.wassbary | Wasserstein Barycenter of SPD Matrices |
| sphere.convert | Convert between Cartesian Coordinates and Geographic... |
| sphere.runif | Generate Uniform Samples on Sphere |
| sphere.utest | Test of Uniformity on Sphere |
| splaplace | Spherical Laplace Distribution |
| spnorm | Spherical Normal Distribution |
| stiefel.optSA | Simulated Annealing on Stiefel Manifold |
| stiefel.runif | Generate Uniform Samples on Stiefel Manifold |
| stiefel.utest | Test of Uniformity on Stiefel Manifold |
| wrap.correlation | Prepare Data on Correlation Manifold |
| wrap.euclidean | Prepare Data on Euclidean Space |
| wrap.grassmann | Prepare Data on Grassmann Manifold |
| wrap.landmark | Wrap Landmark Data on Shape Space |
| wrap.multinomial | Prepare Data on Multinomial Manifold |
| wrap.rotation | Prepare Data on Rotation Group |
| wrap.spd | Prepare Data on Symmetric Positive-Definite (SPD) Manifold |
| wrap.spdk | Prepare Data on SPD Manifold of Fixed-Rank |
| wrap.sphere | Prepare Data on Sphere |
| wrap.stiefel | Prepare Data on (Compact) Stiefel Manifold |
