Dimension Reduction and Estimation Methods

Documentation for package ‘Rdimtools’ version 1.1.2

Help Pages

-- 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