Function reference • Rdimtools

Dimension Estimation Intrinsic dimension estimation algorithms try to estimate the rank/dimension of low-dimensional structure that is embedded in high-dimensional space.
`est.Ustat()`	ID Estimation with Convergence Rate of U-statistic on Manifold
`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
Linear Embedding Although all linear methods are designed to find explicit projection matrix for embedding, we divide this part into two categories; feature selection to select a subset of variables to extract information at their own best measurements, and dimension reduction type to denote the rest.
(2-1) Feature Selection
`do.cscore()`	Constraint Score
`do.cscoreg()`	Constraint Score using Spectral Graph
`do.disr()`	Diversity-Induced Self-Representation
`do.enet()`	Elastic Net Regularization
`do.fosmod()`	Forward Orthogonal Search by Maximizing the Overall Dependency
`do.fscore()`	Fisher Score
`do.lasso()`	Least Absolute Shrinkage and Selection Operator
`do.lscore()`	Laplacian Score
`do.lsdf()`	Locality Sensitive Discriminant Feature
`do.lsls()`	Locality Sensitive Laplacian Score
`do.lspe()`	Locality and Similarity Preserving Embedding
`do.mcfs()`	Multi-Cluster Feature Selection
`do.mifs()`	Mutual Information for Selecting Features
`do.nrsr()`	Non-convex Regularized Self-Representation
`do.pfa()`	Principal Feature Analysis
`do.procrustes()`	Feature Selection using PCA and Procrustes Analysis
`do.rsr()`	Regularized Self-Representation
`do.specs()`	Supervised Spectral Feature Selection
`do.specu()`	Unsupervised Spectral Feature Selection
`do.spufs()`	Structure Preserving Unsupervised Feature Selection
`do.udfs()`	Unsupervised Discriminative Features Selection
`do.ugfs()`	Unsupervised Graph-based Feature Selection
`do.uwdfs()`	Uncorrelated Worst-Case Discriminative Feature Selection
`do.wdfs()`	Worst-Case Discriminative Feature Selection
(2-2) Linear Projection
`do.adr()`	Adaptive Dimension Reduction
`do.ammc()`	Adaptive Maximum Margin Criterion
`do.anmm()`	Average Neighborhood Margin Maximization
`do.asi()`	Adaptive Subspace Iteration
`do.bpca()`	Bayesian Principal Component Analysis
`do.cca()`	Canonical Correlation Analysis
`do.cnpe()`	Complete Neighborhood Preserving Embedding
`do.crp()`	Collaborative Representation-based Projection
`do.dagdne()`	Double-Adjacency Graphs-based Discriminant Neighborhood Embedding
`do.dne()`	Discriminant Neighborhood Embedding
`do.dspp()`	Discriminative Sparsity Preserving Projection
`do.elde()`	Exponential Local Discriminant Embedding
`do.elpp2()`	Enhanced Locality Preserving Projection (2013)
`do.eslpp()`	Extended Supervised Locality Preserving Projection
`do.extlpp()`	Extended Locality Preserving Projection
`do.fa()`	Exploratory Factor Analysis
`do.fssem()`	Feature Subset Selection using Expectation-Maximization
`do.ica()`	Independent Component Analysis
`do.isoproj()`	Isometric Projection
`do.kmvp()`	Kernel-Weighted Maximum Variance Projection
`do.kudp()`	Kernel-Weighted Unsupervised Discriminant Projection
`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.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.lsda()`	Locality Sensitive Discriminant Analysis
`do.lsir()`	Localized Sliced Inverse Regression
`do.lspp()`	Local Similarity Preserving Projection
`do.mds()`	(Classical) Multidimensional Scaling
`do.mfa()`	Marginal Fisher Analysis
`do.mlie()`	Maximal Local Interclass Embedding
`do.mmc()`	Maximum Margin Criterion
`do.mmp()`	Maximum Margin Projection
`do.mmsd()`	Multiple Maximum Scatter Difference
`do.modp()`	Modified Orthogonal Discriminant Projection
`do.msd()`	Maximum Scatter Difference
`do.mvp()`	Maximum Variance 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.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.pflpp()`	Parameter-Free Locality Preserving Projection
`do.pls()`	Partial Least Squares
`do.ppca()`	Probabilistic Principal Component Analysis
`do.rlda()`	Regularized Linear Discriminant Analysis
`do.rndproj()`	Random Projection
`do.rpcag()`	Robust Principal Component Analysis via Geometric Median
`do.rsir()`	Regularized Sliced Inverse Regression
`do.sammc()`	Semi-Supervised Adaptive Maximum Margin Criterion
`do.save()`	Sliced Average Variance Estimation
`do.sda()`	Semi-Supervised Discriminant Analysis
`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.spc()`	Supervised Principal Component Analysis
`do.spca()`	Sparse Principal Component Analysis
`do.spp()`	Sparsity Preserving Projection
`do.ssldp()`	Semi-Supervised Locally Discriminant Projection
`do.udp()`	Unsupervised Discriminant Projection
`do.ulda()`	Uncorrelated Linear Discriminant Analysis
Nonlinear Embedding
`do.bmds()`	Bayesian Multidimensional Scaling
`do.cge()`	Constrained Graph Embedding
`do.cisomap()`	Conformal Isometric Feature Mapping
`do.crca()`	Curvilinear Component Analysis
`do.crda()`	Curvilinear Distance Analysis
`do.dm()`	Diffusion Maps
`do.dppca()`	Dual Probabilistic Principal Component Analysis
`do.dve()`	Distinguishing Variance Embedding
`do.fastmap()`	FastMap
`do.hydra()`	Hyperbolic Distance Recovery and Approximation
`do.idmap()`	Interactive Document Map
`do.iltsa()`	Improved Local Tangent Space Alignment
`do.isomap()`	Isometric Feature Mapping
`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.kpca()`	Kernel Principal Component Analysis
`do.kqmi()`	Kernel Quadratic Mutual Information
`do.ksda()`	Kernel Semi-Supervised Discriminant Analysis
`do.lamp()`	Local Affine Multidimensional Projection
`do.lapeig()`	Laplacian Eigenmaps
`do.lisomap()`	Landmark Isometric Feature Mapping
`do.lle()`	Locally Linear Embedding
`do.llle()`	Local Linear Laplacian Eigenmaps
`do.ltsa()`	Local Tangent Space Alignment
`do.mmds()`	Metric Multidimensional Scaling
`do.mve()`	Minimum Volume Embedding
`do.mvu()`	Maximum Variance Unfolding / Semidefinite Embedding
`do.nnp()`	Nearest Neighbor Projection
`do.phate()`	Potential of Heat Diffusion for Affinity-based Transition Embedding
`do.plp()`	Piecewise Laplacian-based Projection (PLP)
`do.ree()`	Robust Euclidean Embedding
`do.rpca()`	Robust Principal Component Analysis
`do.sammon()`	Sammon Mapping
`do.sne()`	Stochastic Neighbor Embedding
`do.spe()`	Stochastic Proximity Embedding
`do.splapeig()`	Supervised Laplacian Eigenmaps
`do.spmds()`	Spectral Multidimensional Scaling
`do.tsne()`	t-distributed Stochastic Neighbor Embedding
Out-of-sample Prediction
`oos.linproj()`	OOS : Linear Projection
Auxiliary Functions
`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
Data
`iris`	Load Iris data
`usps`	Load USPS handwritten digits data

Reference

(2-1) Feature Selection

(2-2) Linear Projection