# Dimension Reduction and Estimation Methods

## Documentation for package ‘Rdimtools’ version 1.1.3

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A D E I O U

## -- A --

aux.gensamples | Generate model-based samples
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
aux.shortestpath | Find shortest path using Floyd-Warshall algorithm

## -- D --

do.adr | Adaptive Dimension Reduction
do.ammc | Adaptive Maximum Margin Criterion
do.anmm | Average Neighborhood Margin Maximization
do.asi | Adaptive Subspace Iteration
do.bmds | Bayesian Multidimensional Scaling
do.bpca | Bayesian Principal Component Analysis
do.cca | Canonical Correlation Analysis
do.cge | Constrained Graph Embedding
do.cisomap | Conformal Isometric Feature Mapping
do.cnpe | Complete Neighborhood Preserving Embedding
do.crca | Curvilinear Component Analysis
do.crda | Curvilinear Distance Analysis
do.crp | Collaborative Representation-based Projection
do.cscore | Constraint Score
do.cscoreg | Constraint Score using Spectral Graph
do.dagdne | Double-Adjacency Graphs-based Discriminant Neighborhood Embedding
do.disr | Diversity-Induced Self-Representation
do.dm | Diffusion Maps
do.dne | Discriminant Neighborhood Embedding
do.dppca | Dual Probabilistic Principal Component Analysis
do.dspp | Discriminative Sparsity Preserving Projection
do.dve | Distinguishing Variance Embedding
do.elde | Exponential Local Discriminant Embedding
do.elpp2 | Enhanced Locality Preserving Projection (2013)
do.enet | Elastic Net Regularization
do.eslpp | Extended Supervised Locality Preserving Projection
do.extlpp | Extended Locality Preserving Projection
do.fa | Exploratory Factor Analysis
do.fastmap | FastMap
do.fosmod | Forward Orthogonal Search by Maximizing the Overall Dependency
do.fscore | Fisher Score
do.fssem | Feature Subset Selection using Expectation-Maximization
do.hydra | Hyperbolic Distance Recovery and Approximation
do.ica | Independent Component Analysis
do.idmap | Interactive Document Map
do.iltsa | Improved Local Tangent Space Alignment
do.isomap | Isometric Feature Mapping
do.isoproj | Isometric Projection
do.ispe | Isometric Stochastic Proximity Embedding
do.keca | Kernel Entropy Component Analysis
do.klde | Kernel Local Discriminant Embedding
do.klfda | Kernel Local Fisher Discriminant Analysis
do.klsda | Kernel Locality Sensitive Discriminant Analysis
do.kmfa | Kernel Marginal Fisher Analysis
do.kmmc | Kernel Maximum Margin Criterion
do.kmvp | Kernel-Weighted Maximum Variance Projection
do.kpca | Kernel Principal Component Analysis
do.kqmi | Kernel Quadratic Mutual Information
do.ksda | Kernel Semi-Supervised Discriminant Analysis
do.kudp | Kernel-Weighted Unsupervised Discriminant Projection
do.lamp | Local Affine Multidimensional Projection
do.lapeig | Laplacian Eigenmaps
do.lasso | Least Absolute Shrinkage and Selection Operator
do.lda | Linear Discriminant Analysis
do.ldakm | Combination of LDA and K-means
do.lde | Local Discriminant Embedding
do.ldp | Locally Discriminating Projection
do.lea | Locally Linear Embedded Eigenspace Analysis
do.lfda | Local Fisher Discriminant Analysis
do.lisomap | Landmark Isometric Feature Mapping
do.lle | Locally Linear Embedding
do.llle | Local Linear Laplacian Eigenmaps
do.llp | Local Learning Projections
do.lltsa | Linear Local Tangent Space Alignment
do.lmds | Landmark Multidimensional Scaling
do.lpca2006 | Locally Principal Component Analysis by Yang et al. (2006)
do.lpe | Locality Pursuit Embedding
do.lpfda | Locality Preserving Fisher Discriminant Analysis
do.lpmip | Locality-Preserved Maximum Information Projection
do.lpp | Locality Preserving Projection
do.lqmi | Linear Quadratic Mutual Information
do.lscore | Laplacian Score
do.lsda | Locality Sensitive Discriminant Analysis
do.lsdf | Locality Sensitive Discriminant Feature
do.lsir | Localized Sliced Inverse Regression
do.lsls | Locality Sensitive Laplacian Score
do.lspe | Locality and Similarity Preserving Embedding
do.lspp | Local Similarity Preserving Projection
do.ltsa | Local Tangent Space Alignment
do.mcfs | Multi-Cluster Feature Selection
do.mds | (Classical) Multidimensional Scaling
do.mfa | Marginal Fisher Analysis
do.mifs | Mutual Information for Selecting Features
do.mlie | Maximal Local Interclass Embedding
do.mmc | Maximum Margin Criterion
do.mmds | Metric Multidimensional Scaling
do.mmp | Maximum Margin Projection
do.mmsd | Multiple Maximum Scatter Difference
do.modp | Modified Orthogonal Discriminant Projection
do.msd | Maximum Scatter Difference
do.mve | Minimum Volume Embedding
do.mvp | Maximum Variance Projection
do.mvu | Maximum Variance Unfolding / Semidefinite Embedding
do.nnp | Nearest Neighbor Projection
do.nolpp | Nonnegative Orthogonal Locality Preserving Projection
do.nonpp | Nonnegative Orthogonal Neighborhood Preserving Projections
do.npca | Nonnegative Principal Component Analysis
do.npe | Neighborhood Preserving Embedding
do.nrsr | Non-convex Regularized Self-Representation
do.odp | Orthogonal Discriminant Projection
do.olda | Orthogonal Linear Discriminant Analysis
do.olpp | Orthogonal Locality Preserving Projection
do.onpp | Orthogonal Neighborhood Preserving Projections
do.opls | Orthogonal Partial Least Squares
do.pca | Principal Component Analysis
do.pfa | Principal Feature Analysis
do.pflpp | Parameter-Free Locality Preserving Projection
do.phate | Potential of Heat Diffusion for Affinity-based Transition Embedding
do.plp | Piecewise Laplacian-based Projection (PLP)
do.pls | Partial Least Squares
do.ppca | Probabilistic Principal Component Analysis
do.procrustes | Feature Selection using PCA and Procrustes Analysis
do.ree | Robust Euclidean Embedding
do.rlda | Regularized Linear Discriminant Analysis
do.rndproj | Random Projection
do.rpca | Robust Principal Component Analysis
do.rpcag | Robust Principal Component Analysis via Geometric Median
do.rsir | Regularized Sliced Inverse Regression
do.rsr | Regularized Self-Representation
do.sammc | Semi-Supervised Adaptive Maximum Margin Criterion
do.sammon | Sammon Mapping
do.save | Sliced Average Variance Estimation
do.sda | Semi-Supervised Discriminant Analysis
do.sde | Maximum Variance Unfolding / Semidefinite Embedding
do.sdlpp | Sample-Dependent Locality Preserving Projection
do.sir | Sliced Inverse Regression
do.slpe | Supervised Locality Pursuit Embedding
do.slpp | Supervised Locality Preserving Projection
do.sne | Stochastic Neighbor Embedding
do.spc | Supervised Principal Component Analysis
do.spca | Sparse Principal Component Analysis
do.spe | Stochastic Proximity Embedding
do.specs | Supervised Spectral Feature Selection
do.specu | Unsupervised Spectral Feature Selection
do.splapeig | Supervised Laplacian Eigenmaps
do.spmds | Spectral Multidimensional Scaling
do.spp | Sparsity Preserving Projection
do.spufs | Structure Preserving Unsupervised Feature Selection
do.ssldp | Semi-Supervised Locally Discriminant Projection
do.tsne | t-distributed Stochastic Neighbor Embedding
do.udfs | Unsupervised Discriminative Features Selection
do.udp | Unsupervised Discriminant Projection
do.ugfs | Unsupervised Graph-based Feature Selection
do.ulda | Uncorrelated Linear Discriminant Analysis
do.uwdfs | Uncorrelated Worst-Case Discriminative Feature Selection
do.wdfs | Worst-Case Discriminative Feature Selection

## -- E --

est.boxcount | Box-counting Dimension
est.clustering | Intrinsic Dimension Estimation via Clustering
est.correlation | Correlation Dimension
est.danco | Intrinsic Dimensionality Estimation with DANCo
est.gdistnn | Intrinsic Dimension Estimation based on Manifold Assumption and Graph Distance
est.incisingball | Intrinsic Dimension Estimation with Incising Ball
est.made | Manifold-Adaptive Dimension Estimation
est.mindkl | MiNDkl
est.mindml | MINDml
est.mle1 | Maximum Likelihood Esimation with Poisson Process
est.mle2 | Maximum Likelihood Esimation with Poisson Process and Bias Correction
est.nearneighbor1 | Intrinsic Dimension Estimation with Near-Neighbor Information
est.nearneighbor2 | Near-Neighbor Information with Bias Correction
est.packing | Intrinsic Dimension Estimation using Packing Numbers
est.pcathr | PCA Thresholding with Accumulated Variance
est.twonn | Intrinsic Dimension Estimation by a Minimal Neighborhood Information
est.Ustat | ID Estimation with Convergence Rate of U-statistic on Manifold

## -- I --

iris | Load Iris data

## -- O --

oos.linproj | OOS : Linear Projection

## -- U --

usps | Load USPS handwritten digits data