Principal Component Analysis (PCA)

• Probabilistic PCA

• Factor Analysis (FA)

• Sammon mapping

• Linear Discriminant Analysis (LDA)

• Multidimensional scaling (MDS)

• Isomap

• Landmark Isomap

• Local Linear Embedding (LLE)

• Laplacian Eigenmaps

• Hessian LLE

• Local Tangent Space Alignment (LTSA)

• Conformal Eigenmaps (extension of LLE)

• Maximum Variance Unfolding (extension of LLE)

• Landmark MVU (LandmarkMVU)

• Fast Maximum Variance Unfolding (FastMVU)

• Kernel PCA

• Generalized Discriminant Analysis (GDA)

• Diffusion maps

• Neighborhood Preserving Embedding (NPE)

• Locality Preserving Projection (LPP)

• Linear Local Tangent Space Alignment (LLTSA)

• Stochastic Proximity Embedding (SPE)

• Multilayer autoencoders (training by RBM + backpropagation or by an evolutionary algorithm)

• Local Linear Coordination (LLC)

• Manifold charting

• Coordinated Factor Analysis (CFA)

• Gaussian Process Latent Variable Model (GPLVM)

• Stochastic Neighbor Embedding (SNE)

• Symmetric SNE (SymSNE)

• new: t-Distributed Stochastic Neighbor Embedding (t-SNE)

• new: Neighborhood Components Analysis (NCA)

• new: Maximally Collapsing Metric Learning (MCML)