do.cscore()

Constraint Score 
do.cscoreg()

Constraint Score using Spectral Graph 
do.disr()

DiversityInduced SelfRepresentation 
do.enet()

Elastic Net Regularization 
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()

MultiCluster Feature Selection 
do.mifs()

Mutual Information for Selecting Features 
do.nrsr()

Nonconvex Regularized SelfRepresentation 
do.procrustes()

Feature Selection using PCA and Procrustes Analysis 
do.rsr()

Regularized SelfRepresentation 
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 Graphbased Feature Selection 
do.uwdfs()

Uncorrelated WorstCase Discriminative Feature Selection 
do.wdfs()

WorstCase Discriminative Feature Selection 
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 Representationbased Projection 
do.dagdne()

DoubleAdjacency Graphsbased 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 ExpectationMaximization 
do.ica()

Independent Component Analysis 
do.isoproj()

Isometric Projection 
do.kmvp()

KernelWeighted Maximum Variance Projection 
do.kudp()

KernelWeighted Unsupervised Discriminant Projection 
do.lda()

Linear Discriminant Analysis 
do.ldakm()

Combination of LDA and Kmeans 
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()

LocalityPreserved 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()

ParameterFree 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()

SemiSupervised Adaptive Maximum Margin Criterion 
do.save()

Sliced Average Variance Estimation 
do.sda()

SemiSupervised Discriminant Analysis 
do.sdlpp()

SampleDependent 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()

SemiSupervised Locally Discriminant Projection 
do.udp()

Unsupervised Discriminant Projection 
do.ulda()

Uncorrelated Linear Discriminant Analysis 
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.dve()

Distinguishing Variance Embedding 
do.fastmap()

FastMap 
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 SemiSupervised 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 Affinitybased Transition Embedding 
do.plp()

Piecewise Laplacianbased 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()

tdistributed Stochastic Neighbor Embedding 