Rdimtools1.1.3 package

Generate model-based samples

Construct Nearest-Neighborhood Graph

Build a centered kernel matrix K

Show the number of functions for Rdimtools.

Preprocessing the data

Find shortest path using Floyd-Warshall algorithm

Box-counting Dimension

Intrinsic Dimension Estimation via Clustering

Correlation Dimension

Intrinsic Dimensionality Estimation with DANCo

Intrinsic Dimension Estimation based on Manifold Assumption and Graph ...

Intrinsic Dimension Estimation with Incising Ball

Manifold-Adaptive Dimension Estimation

MiNDkl

MINDml

Maximum Likelihood Esimation with Poisson Process

Maximum Likelihood Esimation with Poisson Process and Bias Correction

Intrinsic Dimension Estimation with Near-Neighbor Information

Near-Neighbor Information with Bias Correction

Intrinsic Dimension Estimation using Packing Numbers

PCA Thresholding with Accumulated Variance

Intrinsic Dimension Estimation by a Minimal Neighborhood Information

ID Estimation with Convergence Rate of U-statistic on Manifold

Constraint Score

Constraint Score using Spectral Graph

Diversity-Induced Self-Representation

Elastic Net Regularization

Forward Orthogonal Search by Maximizing the Overall Dependency

Fisher Score

Least Absolute Shrinkage and Selection Operator

Laplacian Score

Locality Sensitive Discriminant Feature

Locality Sensitive Laplacian Score

Locality and Similarity Preserving Embedding

Multi-Cluster Feature Selection

Mutual Information for Selecting Features

Non-convex Regularized Self-Representation

Principal Feature Analysis

Feature Selection using PCA and Procrustes Analysis

Regularized Self-Representation

Supervised Spectral Feature Selection

Unsupervised Spectral Feature Selection

Structure Preserving Unsupervised Feature Selection

Unsupervised Discriminative Features Selection

Unsupervised Graph-based Feature Selection

Uncorrelated Worst-Case Discriminative Feature Selection

Worst-Case Discriminative Feature Selection

Adaptive Dimension Reduction

Adaptive Maximum Margin Criterion

Average Neighborhood Margin Maximization

Adaptive Subspace Iteration

Bayesian Principal Component Analysis

Canonical Correlation Analysis

Complete Neighborhood Preserving Embedding

Collaborative Representation-based Projection

Double-Adjacency Graphs-based Discriminant Neighborhood Embedding

Discriminant Neighborhood Embedding

Discriminative Sparsity Preserving Projection

Exponential Local Discriminant Embedding

Enhanced Locality Preserving Projection (2013)

Extended Supervised Locality Preserving Projection

Extended Locality Preserving Projection

Exploratory Factor Analysis

Feature Subset Selection using Expectation-Maximization

Independent Component Analysis

Isometric Projection

Kernel-Weighted Maximum Variance Projection

Kernel-Weighted Unsupervised Discriminant Projection

Linear Discriminant Analysis

Combination of LDA and K-means

Local Discriminant Embedding

Locally Discriminating Projection

Locally Linear Embedded Eigenspace Analysis

Local Fisher Discriminant Analysis

Local Learning Projections

Linear Local Tangent Space Alignment

Landmark Multidimensional Scaling

Locally Principal Component Analysis by Yang et al. (2006)

Locality Pursuit Embedding

Locality Preserving Fisher Discriminant Analysis

Locality-Preserved Maximum Information Projection

Locality Preserving Projection

Linear Quadratic Mutual Information

Locality Sensitive Discriminant Analysis

Localized Sliced Inverse Regression

Local Similarity Preserving Projection

(Classical) Multidimensional Scaling

Marginal Fisher Analysis

Maximal Local Interclass Embedding

Maximum Margin Criterion

Maximum Margin Projection

Multiple Maximum Scatter Difference

Modified Orthogonal Discriminant Projection

Maximum Scatter Difference

Maximum Variance Projection

Nonnegative Orthogonal Locality Preserving Projection

Nonnegative Orthogonal Neighborhood Preserving Projections

Nonnegative Principal Component Analysis

Neighborhood Preserving Embedding

Orthogonal Discriminant Projection

Orthogonal Linear Discriminant Analysis

Orthogonal Locality Preserving Projection

Orthogonal Neighborhood Preserving Projections

Orthogonal Partial Least Squares

Principal Component Analysis

Parameter-Free Locality Preserving Projection

Partial Least Squares

Probabilistic Principal Component Analysis

Regularized Linear Discriminant Analysis

Random Projection

Robust Principal Component Analysis via Geometric Median

Regularized Sliced Inverse Regression

Semi-Supervised Adaptive Maximum Margin Criterion

Sliced Average Variance Estimation

Semi-Supervised Discriminant Analysis

Sample-Dependent Locality Preserving Projection

Sliced Inverse Regression

Supervised Locality Pursuit Embedding

Supervised Locality Preserving Projection

Supervised Principal Component Analysis

Sparse Principal Component Analysis

Sparsity Preserving Projection

Semi-Supervised Locally Discriminant Projection

Unsupervised Discriminant Projection

Uncorrelated Linear Discriminant Analysis

Bayesian Multidimensional Scaling

Constrained Graph Embedding

Conformal Isometric Feature Mapping

Curvilinear Component Analysis

Curvilinear Distance Analysis

Diffusion Maps

Dual Probabilistic Principal Component Analysis

Distinguishing Variance Embedding

FastMap

Hyperbolic Distance Recovery and Approximation

Interactive Document Map

Improved Local Tangent Space Alignment

Isometric Feature Mapping

Isometric Stochastic Proximity Embedding

Kernel Entropy Component Analysis

Kernel Local Discriminant Embedding

Kernel Local Fisher Discriminant Analysis

Kernel Locality Sensitive Discriminant Analysis

Kernel Marginal Fisher Analysis

Kernel Maximum Margin Criterion

Kernel Principal Component Analysis

Kernel Quadratic Mutual Information

Kernel Semi-Supervised Discriminant Analysis

Local Affine Multidimensional Projection

Laplacian Eigenmaps

Landmark Isometric Feature Mapping

Locally Linear Embedding

Local Linear Laplacian Eigenmaps

Local Tangent Space Alignment

Metric Multidimensional Scaling

Minimum Volume Embedding

Maximum Variance Unfolding / Semidefinite Embedding

Nearest Neighbor Projection

Potential of Heat Diffusion for Affinity-based Transition Embedding

Piecewise Laplacian-based Projection (PLP)

Robust Euclidean Embedding

Robust Principal Component Analysis

Sammon Mapping

Stochastic Neighbor Embedding

Stochastic Proximity Embedding

Supervised Laplacian Eigenmaps

Spectral Multidimensional Scaling

t-distributed Stochastic Neighbor Embedding

OOS : Linear Projection