Learn R Programming
⚠️There's a newer version (1.1.2) of this package.Take me there.

Rdimtools Development Repo

Rdimtools is an R package for Dimension Reduction (also known as Manifold Learning) and intrinsic Dimension Estimation methods.

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")

Copy Link

Version

Install

install.packages('Rdimtools')

Monthly Downloads

608

Version

0.3.2

License

GPL (>= 3)

Issues

4

Pull Requests

0

Stars

54

Forks

10

Maintainer

Kisung You

Last Published

March 6th, 2018

Functions in Rdimtools (0.3.2)

aux.graphnbd

Construct Nearest-Neighborhood Graph
do.cca

Canonical Correlation Analysis
do.cnpe

Complete Neighborhood Preserving Embedding
do.isoproj

Isometric Projection
do.kmvp

Kernel-Weighted Maximum Variance Projection
do.lde

Local Discriminant Embedding
do.ldp

Locally Discriminating Projection
aux.pkgstat

Show the number of functions for Rdimtools.
aux.preprocess

Preprocessing the data
Rdimtools

Dimension Reduction and Estimation Methods
do.dne

Discriminant Neighborhood Embedding
aux.gensamples

Generate model-based samples
do.dspp

Discriminative Sparsity Preserving Projection
do.extlpp

Extended Locality Preserving Projection
do.fa

Exploratory Factor Analysis
do.lpmip

Locality-Preserved Maximum Information Projection
do.lpp

Locality Preserving Projection
do.crp

Collaborative Representation-based Projection
do.dagdne

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

Locally Linear Embedded Eigenspace Analysis
do.lfda

Local Fisher Discriminant Analysis
do.elde

Exponential Local Discriminant Embedding
do.lsda

Locality Sensitive Discriminant Analysis
aux.shortestpath

Find shortest path using Floyd-Warshall algorithm
do.elpp2

Enhanced Locality Preserving Projection (2013)
est.boxcount

Box-counting dimension
do.mmsd

Multiple Maximum Scatter Difference
do.lsdf

Locality Sensitive Discriminant Feature
do.kudp

Kernel-Weighted Unsupervised Discriminant Projection
do.lasso

Least Absolute Shrinkage and Selection Operator
do.modp

Modified Orthogonal Discriminant Projection
est.correlation

Correlation Dimension
do.adr

Adaptive Dimension Reduction
do.ammc

Adaptive Maximum Margin Criterion
do.anmm

Average Neighborhood Margin Maximization
do.asi

Adaptive Subspace Iteration
do.enet

Elastic Net Regularization
do.eslpp

Extended Supervised Locality Preserving Projection
do.bpca

Bayesian Principal Component Analysis
do.nolpp

Nonnegative Orthogonal Locality Preserving Projection
do.mfa

Marginal Fisher Analysis
do.mlie

Maximal Local Interclass Embedding
do.nonpp

Nonnegative Orthogonal Neighborhood Preserving Projections
do.pflpp

Parameter-Free Locality Preserving Projection
do.pls

Partial Least Squares
do.lmds

Landmark Multidimensional Scaling
do.lpca

Locally Principal Component Analysis
do.ssldp

Semi-Supervised Locally Discriminant Projection
do.llp

Local Learning Projections
do.lltsa

Linear Local Tangent Space Alignment
do.udp

Unsupervised Discriminant Projection
do.fscore

Fisher Score
do.olpp

Orthogonal Locality Preserving Projection
do.keca

Kernel Entropy Component Analysis
do.klde

Kernel Local Discriminant Embedding
do.ica

Independent Component Analysis
do.lda

Linear Discriminant Analysis
do.onpp

Orthogonal Neighborhood Preserving Projections
do.mcfs

Multi-Cluster Feature Selection
do.mds

(Classical) Multidimensional Scaling
do.opls

Orthogonal Partial Least Squares
do.ldakm

Combination of LDA and K-means
do.pca

Principal Component Analysis
do.ulda

Uncorrelated Linear Discriminant Analysis
do.lqmi

Linear Quadratic Mutual Information
do.rlda

Regularized Linear Discriminant Analysis
do.lscore

Laplacian Score
do.cisomap

Conformal Isometric Feature Mapping
do.crca

Curvilinear Component Analysis
do.ksda

Kernel Semi-Supervised Discriminant Analysis
do.lapeig

Laplacian Eigenmaps
do.lpe

Locality Pursuit Embedding
do.lpfda

Locality Preserving Fisher Discriminant Analysis
do.kmfa

Kernel Marginal Fisher Analysis
do.kmmc

Kernel Maximum Margin Criterion
do.mmp

Maximum Margin Projection
do.mmc

Maximum Margin Criterion
do.msd

Maximum Scatter Difference
do.mvp

Maximum Variance Projection
do.lsir

Localized Sliced Inverse Regression
do.save

Sliced Average Variance Estimation
do.lspp

Local Similarity Preserving Projection
do.spe

Stochastic Proximity Embedding
do.splapeig

Supervised Laplacian Eigenmaps
do.sda

Semi-Supervised Discriminant Analysis
do.spca

Sparse Principal Component Analysis
do.ree

Robust Euclidean Embedding
do.rpca

Robust Principal Component Analysis
do.spp

Sparsity Preserving Projection
do.crda

Curvilinear Distance Analysis
do.odp

Orthogonal Discriminant Projection
do.dm

Diffusion Maps
do.olda

Orthogonal Linear Discriminant Analysis
do.npca

Nonnegative Principal Component Analysis
do.npe

Neighborhood Preserving Embedding
do.rsir

Regularized Sliced Inverse Regression
do.kpca

Kernel Principal Component Analysis
do.sdlpp

Sample-Dependent Locality Preserving Projection
do.sammc

Semi-Supervised Adaptive Maximum Margin Criterion
do.sir

Sliced Inverse Regression
do.kqmi

Kernel Quadratic Mutual Information
do.dve

Distinguishing Variance Embedding
oos.linproj

OOS : Linear Projection
do.iltsa

Improved Local Tangent Space Alignment
do.ppca

Probabilistic Principal Component Analysis
do.klfda

Kernel Local Fisher Discriminant Analysis
do.klsda

Kernel Locality Sensitive Discriminant Analysis
do.rndproj

Random Projection
do.slpe

Supervised Locality Pursuit Embedding
do.slpp

Supervised Locality Preserving Projection
do.isomap

Isometric Feature Mapping
do.mvu

Maximum Variance Unfolding / Semidefinite Embedding
do.plp

Piecewise Laplacian-based Projection (PLP)
do.sammon

Sammon Mapping
do.sne

Stochastic Neighbor Embedding
do.tsne

t-distributed Stochastic Neighbor Embedding
do.ispe

Isometric Stochastic Proximity Embedding
oos.linear

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

Landmark Isometric Feature Mapping
do.lle

Locally Linear Embedding
do.ltsa

Local Tangent Space Alignment
do.mve

Minimum Volume Embedding
aux.kernelcov

Build a centered kernel matrix K