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Rdimtools (version 0.1.2)

Dimension Reduction and Estimation Methods

Description

We provide a rich collection of linear and nonlinear dimension reduction techniques implemented using 'RcppArmadillo'. The question on what we should use as the target dimension is addressed by intrinsic dimension estimation methods introduced as well. For more details on dimensionality techniques, see the paper by Ma and Zhu (2013) if you are interested in statistical approach, or Engel, Huttenberger, and Hamann (2012) for a broader cross-disciplinary overview.

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Version

Install

install.packages('Rdimtools')

Monthly Downloads

620

Version

0.1.2

License

GPL (>= 3)

Maintainer

Kisung You

Last Published

November 14th, 2017

Functions in Rdimtools (0.1.2)

aux.preprocess

Centering, decorrelating, or whitening of the data
aux.shortestpath

Find shortest path using Floyd-Warshall algorithm
Rdimtools

Dimension Reduction and Estimation Methods
do.ica

Independent Component Analysis
do.fa

Exploratory Factor Analysis
est.boxcount

Box-counting dimension
aux.gensamples

Generate model-based samples
est.correlation

Correlation Dimension
aux.graphnbd

Find nearest neighborhood
do.rndproj

Random Projection
aux.kernelcov

Build a centered kernel matrix K
do.dm

Diffusion Maps
do.isomap

Isometric Feature Mapping
do.lda

Linear Discriminant Analysis
do.lmds

Landmark Multidimensional Scaling
do.npe

Neighborhood Preserving Embedding
do.pca

Principal Component Analysis
do.lpp

Locality Preserving Projections
do.cisomap

Conformal Isometric Feature Mapping
do.mvu

Maximum Variance Unfolding / Semidefinite Embedding
do.mds

(Classical) Multidimensional Scaling
do.ree

Robust Euclidean Embedding
do.lle

Locally-Linear Embedding
do.lapeig

Laplacian Eigenmaps
do.lisomap

Landmark Isometric Feature Mapping
do.ltsa

Local Tangent Space Alignment
do.sammon

Sammon Mapping
do.keca

Kernel Entropy Component Analysis
do.sne

Stochastic Neighbor Embedding
do.kpca

Kernel Principal Component Analysis
do.tsne

t-distributed Stochastic Neighbor Embedding
do.plp

Piecewise Laplacian-based Projection (PLP)