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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 0.4.2 provides 125 manifold learning methods and 13 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("kisungyou/Rdimtools")

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

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Version

Install

install.packages('Rdimtools')

Monthly Downloads

625

Version

0.4.1

License

GPL-3

Issues

4

Pull Requests

0

Stars

54

Forks

10

Maintainer

Kisung You

Last Published

November 15th, 2018

Functions in Rdimtools (0.4.1)

est.correlation

Correlation Dimension
est.made

Manifold-Adaptive Dimension Estimation
est.mle1

Maximum Likelihood Esimation with Poisson Process
do.cnpe

Complete Neighborhood Preserving Embedding
do.cca

Canonical Correlation Analysis
do.enet

Elastic Net Regularization
do.elpp2

Enhanced Locality Preserving Projection (2013)
est.nearneighbor1

Intrinsic Dimension Estimation with Near-Neighbor Information
do.adr

Adaptive Dimension Reduction
est.pcathr

PCA Thresholding with Accumulated Variance
do.ldakm

Combination of LDA and K-means
est.boxcount

Box-counting Dimension
est.clustering

Intrinsic Dimension Estimation via Clustering
do.fscore

Fisher Score
do.fa

Exploratory Factor Analysis
do.eslpp

Extended Supervised Locality Preserving Projection
do.lfda

Local Fisher Discriminant Analysis
est.mle2

Maximum Likelihood Esimation with Poisson Process and Bias Correction
est.packing

Intrinsic Dimension Estimation using Packing Numbers
do.disr

Diversity-Induced Self-Representation
est.nearneighbor2

Near-Neighbor Information with Bias Correction
do.llp

Local Learning Projections
est.Ustat

ID Estimation with Convergence Rate of U-statistic on Manifold
est.incisingball

Intrinsic Dimension Estimation with Incising Ball
do.extlpp

Extended Locality Preserving Projection
aux.gensamples

Generate model-based samples
do.asi

Adaptive Subspace Iteration
est.twonn

Intrinsic Dimension Estimation by a Minimal Neighborhood Information
do.dne

Discriminant Neighborhood Embedding
do.lltsa

Linear Local Tangent Space Alignment
do.bpca

Bayesian Principal Component Analysis
do.dspp

Discriminative Sparsity Preserving Projection
do.mcfs

Multi-Cluster Feature Selection
do.elde

Exponential Local Discriminant Embedding
do.lmds

Landmark Multidimensional Scaling
do.lasso

Least Absolute Shrinkage and Selection Operator
do.lde

Local Discriminant Embedding
do.ammc

Adaptive Maximum Margin Criterion
do.lda

Linear Discriminant Analysis
do.ldp

Locally Discriminating Projection
do.mds

(Classical) Multidimensional Scaling
do.lpfda

Locality Preserving Fisher Discriminant Analysis
do.kmvp

Kernel-Weighted Maximum Variance Projection
do.lpmip

Locality-Preserved Maximum Information Projection
do.kudp

Kernel-Weighted Unsupervised Discriminant Projection
do.nrsr

Non-convex Regularized Self-Representation
do.lea

Locally Linear Embedded Eigenspace Analysis
do.odp

Orthogonal Discriminant Projection
do.msd

Maximum Scatter Difference
do.anmm

Average Neighborhood Margin Maximization
do.crp

Collaborative Representation-based Projection
do.ssldp

Semi-Supervised Locally Discriminant Projection
do.lpca

Locally Principal Component Analysis
do.dagdne

Double-Adjacency Graphs-based Discriminant Neighborhood Embedding
do.lpp

Locality Preserving Projection
do.lpe

Locality Pursuit Embedding
do.lqmi

Linear Quadratic Mutual Information
do.ica

Independent Component Analysis
do.mvp

Maximum Variance Projection
do.lsdf

Locality Sensitive Discriminant Feature
do.isoproj

Isometric Projection
do.sdlpp

Sample-Dependent Locality Preserving Projection
do.lsir

Localized Sliced Inverse Regression
do.lscore

Laplacian Score
do.mfa

Marginal Fisher Analysis
do.udfs

Unsupervised Discriminative Features Selection
do.dm

Diffusion Maps
do.lsda

Locality Sensitive Discriminant Analysis
do.mmsd

Multiple Maximum Scatter Difference
do.lspe

Locality and Similarity Preserving Embedding
do.modp

Modified Orthogonal Discriminant Projection
do.olda

Orthogonal Linear Discriminant Analysis
do.udp

Unsupervised Discriminant Projection
do.sir

Sliced Inverse Regression
do.nolpp

Nonnegative Orthogonal Locality Preserving Projection
do.olpp

Orthogonal Locality Preserving Projection
do.save

Sliced Average Variance Estimation
do.lspp

Local Similarity Preserving Projection
do.dve

Distinguishing Variance Embedding
do.mlie

Maximal Local Interclass Embedding
do.nonpp

Nonnegative Orthogonal Neighborhood Preserving Projections
do.ulda

Uncorrelated Linear Discriminant Analysis
do.kpca

Kernel Principal Component Analysis
do.pca

Principal Component Analysis
do.sda

Semi-Supervised Discriminant Analysis
do.crca

Curvilinear Component Analysis
do.npca

Nonnegative Principal Component Analysis
do.crda

Curvilinear Distance Analysis
do.klfda

Kernel Local Fisher Discriminant Analysis
do.mmc

Maximum Margin Criterion
do.mmp

Maximum Margin Projection
do.onpp

Orthogonal Neighborhood Preserving Projections
do.kqmi

Kernel Quadratic Mutual Information
do.pflpp

Parameter-Free Locality Preserving Projection
do.lapeig

Laplacian Eigenmaps
do.rndproj

Random Projection
do.lisomap

Landmark Isometric Feature Mapping
do.tsne

t-distributed Stochastic Neighbor Embedding
do.npe

Neighborhood Preserving Embedding
do.opls

Orthogonal Partial Least Squares
do.spc

Supervised Principal Component Analysis
do.rsr

Regularized Self-Representation
oos.linear

Out-Of-Sample Prediction for Linear Methods
do.sammc

Semi-Supervised Adaptive Maximum Margin Criterion
do.spca

Sparse Principal Component Analysis
do.pls

Partial Least Squares
do.isomap

Isometric Feature Mapping
do.ppca

Probabilistic Principal Component Analysis
do.rsir

Regularized Sliced Inverse Regression
do.spp

Sparsity Preserving Projection
do.klsda

Kernel Locality Sensitive Discriminant Analysis
do.ispe

Isometric Stochastic Proximity Embedding
do.slpe

Supervised Locality Pursuit Embedding
do.slpp

Supervised Locality Preserving Projection
do.rlda

Regularized Linear Discriminant Analysis
do.keca

Kernel Entropy Component Analysis
do.plp

Piecewise Laplacian-based Projection (PLP)
do.spufs

Structure Preserving Unsupervised Feature Selection
do.klde

Kernel Local Discriminant Embedding
do.lle

Locally Linear Embedding
do.ree

Robust Euclidean Embedding
do.fastmap

FastMap
do.sne

Stochastic Neighbor Embedding
do.ltsa

Local Tangent Space Alignment
do.iltsa

Improved Local Tangent Space Alignment
do.kmfa

Kernel Marginal Fisher Analysis
do.cisomap

Conformal Isometric Feature Mapping
do.kmmc

Kernel Maximum Margin Criterion
do.rpca

Robust Principal Component Analysis
do.spe

Stochastic Proximity Embedding
do.sammon

Sammon Mapping
do.splapeig

Supervised Laplacian Eigenmaps
do.ksda

Kernel Semi-Supervised Discriminant Analysis
do.lamp

Local Affine Multidimensional Projection
do.mve

Minimum Volume Embedding
do.spmds

Spectral Multidimensional Scaling
do.mvu

Maximum Variance Unfolding / Semidefinite Embedding
oos.linproj

OOS : Linear Projection
aux.graphnbd

Construct Nearest-Neighborhood Graph
aux.kernelcov

Build a centered kernel matrix K
aux.pkgstat

Show the number of functions for Rdimtools.
aux.preprocess

Preprocessing the data
Rdimtools

Dimension Reduction and Estimation Methods
aux.shortestpath

Find shortest path using Floyd-Warshall algorithm
do.nnp

Nearest Neighbor Projection
do.llle

Local Linear Laplacian Eigenmaps
do.idmap

Interactive Document Map