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Rdimtools : Dimension Reduction and Estimation Methods

Rdimtools is an R package for dimension reduction, manifold learning, and intrnsic dimension estimation methods. Current version 1.0.0 provides 133 manifold learning methods and 17 intrinsic dimension estimation methods.

The philosophy is simple, the more we have at hands, the better we can play.

Installation

You can install a release version from CRAN:

install.packages("Rdimtools")

or the development version from github:

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

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Version

Install

install.packages('Rdimtools')

Monthly Downloads

608

Version

1.0.0

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Repository

http://github.com/kyoustat/Rdimtools

Maintainer

Kisung You

Last Published

April 14th, 2020

Functions in Rdimtools (1.0.0)

Construct Nearest-Neighborhood Graph

Preprocessing the data

aux.shortestpath

Find shortest path using Floyd-Warshall algorithm

Box-counting Dimension

Intrinsic Dimension Estimation via Clustering

Dimension Reduction and Estimation Methods

Generate model-based samples

Show the number of functions for Rdimtools.

Maximum Likelihood Esimation with Poisson Process

ID Estimation with Convergence Rate of U-statistic on Manifold

Adaptive Dimension Reduction

Adaptive Maximum Margin Criterion

est.nearneighbor2

Near-Neighbor Information with Bias Correction

Maximum Likelihood Esimation with Poisson Process and Bias Correction

Double-Adjacency Graphs-based Discriminant Neighborhood Embedding

Diversity-Induced Self-Representation

Intrinsic Dimension Estimation based on Manifold Assumption and Graph Distance

est.nearneighbor1

Intrinsic Dimension Estimation with Near-Neighbor Information

Build a centered kernel matrix K

Intrinsic Dimension Estimation using Packing Numbers

est.correlation

Correlation Dimension

Intrinsic Dimensionality Estimation with DANCo

Average Neighborhood Margin Maximization

Feature Subset Selection using Expectation-Maximization

PCA Thresholding with Accumulated Variance

Exponential Local Discriminant Embedding

Intrinsic Dimension Estimation by a Minimal Neighborhood Information

Manifold-Adaptive Dimension Estimation

Complete Neighborhood Preserving Embedding

Adaptive Subspace Iteration

est.incisingball

Intrinsic Dimension Estimation with Incising Ball

Collaborative Representation-based Projection

Bayesian Principal Component Analysis

Enhanced Locality Preserving Projection (2013)

Extended Locality Preserving Projection

Constraint Score

Canonical Correlation Analysis

Kernel-Weighted Maximum Variance Projection

Local Fisher Discriminant Analysis

Kernel-Weighted Unsupervised Discriminant Projection

Linear Local Tangent Space Alignment

Discriminant Neighborhood Embedding

Landmark Multidimensional Scaling

Local Learning Projections

Locally Principal Component Analysis

Discriminative Sparsity Preserving Projection

Exploratory Factor Analysis

Independent Component Analysis

Constraint Score using Spectral Graph

Locality and Similarity Preserving Embedding

Locality Pursuit Embedding

Elastic Net Regularization

Extended Supervised Locality Preserving Projection

Local Similarity Preserving Projection

Nonnegative Principal Component Analysis

Combination of LDA and K-means

Local Discriminant Embedding

Isometric Projection

Least Absolute Shrinkage and Selection Operator

Locally Discriminating Projection

Laplacian Score

Locality Sensitive Discriminant Analysis

Neighborhood Preserving Embedding

Locally Linear Embedded Eigenspace Analysis

Modified Orthogonal Discriminant Projection

Regularized Linear Discriminant Analysis

Multiple Maximum Scatter Difference

Locality Sensitive Discriminant Feature

Linear Discriminant Analysis

Localized Sliced Inverse Regression

Locality Preserving Fisher Discriminant Analysis

Nonnegative Orthogonal Neighborhood Preserving Projections

Sliced Inverse Regression

Robust Principal Component Analysis via Geometric Median

Regularized Sliced Inverse Regression

Sample-Dependent Locality Preserving Projection

Nonnegative Orthogonal Locality Preserving Projection

Locality-Preserved Maximum Information Projection

Marginal Fisher Analysis

Maximal Local Interclass Embedding

Orthogonal Linear Discriminant Analysis

Unsupervised Discriminant Projection

Maximum Margin Criterion

Bayesian Multidimensional Scaling

Curvilinear Distance Analysis

Constrained Graph Embedding

Semi-Supervised Adaptive Maximum Margin Criterion

Random Projection

Regularized Self-Representation

Maximum Margin Projection

Probabilistic Principal Component Analysis

Partial Least Squares

Orthogonal Locality Preserving Projection

Non-convex Regularized Self-Representation

Orthogonal Discriminant Projection

Supervised Principal Component Analysis

Semi-Supervised Locally Discriminant Projection

Sparse Principal Component Analysis

Distinguishing Variance Embedding

Unsupervised Discriminative Features Selection

Locality Preserving Projection

Principal Component Analysis

Uncorrelated Linear Discriminant Analysis

Parameter-Free Locality Preserving Projection

Local Affine Multidimensional Projection

Isometric Feature Mapping

Kernel Semi-Supervised Discriminant Analysis

Local Tangent Space Alignment

Multi-Cluster Feature Selection

Linear Quadratic Mutual Information

Sliced Average Variance Estimation

Semi-Supervised Discriminant Analysis

(Classical) Multidimensional Scaling

Minimum Volume Embedding

t-distributed Stochastic Neighbor Embedding

Out-Of-Sample Prediction for Linear Methods

Supervised Locality Preserving Projection

Supervised Locality Pursuit Embedding

Orthogonal Partial Least Squares

Orthogonal Neighborhood Preserving Projections

Maximum Scatter Difference

Maximum Variance Projection

Supervised Spectral Feature Selection

Unsupervised Spectral Feature Selection

Isometric Stochastic Proximity Embedding

Curvilinear Component Analysis

Structure Preserving Unsupervised Feature Selection

Conformal Isometric Feature Mapping

Sparsity Preserving Projection

Kernel Marginal Fisher Analysis

Kernel Maximum Margin Criterion

Kernel Principal Component Analysis

Kernel Quadratic Mutual Information

Supervised Laplacian Eigenmaps

Maximum Variance Unfolding / Semidefinite Embedding

Nearest Neighbor Projection

Laplacian Eigenmaps

Spectral Multidimensional Scaling

Landmark Isometric Feature Mapping

Stochastic Proximity Embedding

Stochastic Neighbor Embedding

Interactive Document Map

Kernel Entropy Component Analysis

Improved Local Tangent Space Alignment

Locally Linear Embedding

Kernel Local Discriminant Embedding

Local Linear Laplacian Eigenmaps

OOS : Linear Projection

Kernel Local Fisher Discriminant Analysis

Kernel Locality Sensitive Discriminant Analysis

Robust Principal Component Analysis

Piecewise Laplacian-based Projection (PLP)

Robust Euclidean Embedding