Section 4.1.2.1.0 - Basic Writeup - Cunningham,Ghahramani - Linear Dimensionality Reduction: Survey, Insights and Generalizations
Section 4.1.2.1.1 - Principal component analysis (PCA) - Unsupervised
Section 4.1.2.1.1.0 - Introductory Writeups
Section 4.1.2.1.1.0.1 - Basic Writeup - Wikipedia - Principal Component Analysis
Section 4.1.2.1.1.0.2 - Detailed Writeup - Abdi. Williams - Principal Component Analysis
Section 4.1.2.1.1.1 - Robust Principal Component Analysis (rPCA)
Section 4.1.2.1.1.2 - Kernel Principal Component Analysis
Section 4.1.2.1.1.3 - Multilinear Principal Component Analysis
Section 4.1.2.1.2 - Independent Component Analysis (ICA)
Section 4.1.2.1.2.0 - Introductory Writeups
Section 4.1.2.1.2.0.1 - Basic Writeup - Wikipedia - Independent Component Analysis
Section 4.1.2.1.2.0.2 - Detailed Writeup - Izenman - What is Independent Component Analysis?
Section 4.1.2.1.2.1 - Linear noiseless ICA
Section 4.1.2.1.2.2 - Linear noisy ICA
Section 4.1.2.1.2.3 - Nonlinear ICA
Section 4.1.2.1.2.4 - Linear and Nonlinear Mixtures - MISEP ICA
Section 4.1.2.1.3 - Fishers Linear Discriminant - Supervised
Section 4.1.2.1.3.0 - Basic Writeup - Jianxin Wu - Fishers's Linear Discriminant
Section 4.1.2.1.3.1 - Linear Discriminant Analysis
Section 4.1.2.1.3.2 - Generalized(Kernel Fisher) discriminant analysis (GDA)
Section 4.1.2.1.4 - Canonical correlation analysis (CCA)
Section 4.1.2.1.5 - Factor analysis
Section 4.1.2.1.5.0 - Basic Writeup - Wikipedia - Factor Analysis
Section 4.1.2.1.5.1 - Common Factor Analysis
Section 4.1.2.1.5.1.0 - Basic Writeup - Stackexchange - Common Factor Analysis
Section 4.1.2.1.5.1.1 - Principal Axis Factoring (PAF)
Section 4.1.2.1.5.1.1.0 - Basic Writeup - Stackexchange - Principal Axis Factoring (PAF)
Section 4.1.2.1.5.1.1.1 - Principal Factor Analysis
Section 4.1.2.1.5.1.1.2 - Iterative Principal Factor Analysis
Section 4.1.2.1.5.1.2 - Correlation-Fitting Factoring Methods
Section 4.1.2.1.5.1.2.1 - Ordinary Least Squares
Section 4.1.2.1.5.1.2.2 - Generalized Least Squares
Section 4.1.2.1.5.1.2.3 - Maximum Likelihood
Section 4.1.2.1.5.2 - Exploratory factor analysis (EFA)
Section 4.1.2.1.5.2.0 - Introductory Writeups
Section 4.1.2.1.5.2.0.1 - Basic Writeup - Wikipedia - Exploratory Factor Analysis
Section 4.1.2.1.5.2.0.2 - Detailed Writeup - Koostra - Exploratory Factor Analysis
Section 4.1.2.1.5.3 - Confirmatory factor analysis (CFA)
Section 4.1.2.1.6 - Multilinear Subspace Learning
Section 4.1.2.1.7 - Matrix Decomposition(Factorization)
Section 4.1.2.2.1 - Multidimensional scaling (MDS)
Section 4.1.2.2.2 - Sammon's mapping
Section 4.1.2.2.3 - Self-organizing map
Section 4.1.2.2.4 - Principal curves on manifolds
Section 4.1.2.2.5 - Autoencoders
Section 4.1.2.2.6 - Gaussian process latent variable models
Section 4.1.2.2.7 - ISOMAP
Section 4.1.2.2.8 - Curvilinear component analysis
Section 4.1.2.2.9 - Curvilinear distance analysis
Section 4.1.2.2.10 - Diffeomorphic dimensionality reduction
Section 4.1.2.2.11 - Kernel principal component analysis
Section 4.1.2.2.12 - Contagion maps
Section 4.1.2.2.13 - Locally-linear embedding
Section 4.1.2.2.14 - Laplacian eigenmaps
Section 4.1.2.2.15 - Manifold alignment
Section 4.1.2.2.16 - Diffusion maps
Section 4.1.2.2.17 - Hessian Locally-Linear Embedding (Hessian LLE)
Section 4.1.2.2.18 - Modified Locally-Linear Embedding (MLLE)
Section 4.1.2.2.19 - Relational perspective map
Section 4.1.2.2.20 - Local tangent space alignment
Section 4.1.2.2.21 - Local multidimensional scaling
Section 4.1.2.2.22 - Maximum variance unfolding
Section 4.1.2.2.23 - Nonlinear PCA
Section 4.1.2.2.24 - Data-driven high-dimensional scaling
Section 4.1.2.2.25 - Manifold sculpting
Section 4.1.2.2.26 - t-distributed stochastic neighbor embedding
Section 4.1.2.2.27 - RankVisu
Section 4.1.2.2.28 - Topologically constrained isometric embedding
Section 4.1.2.2.29 - Semidefinite embedding
Section 4.1.2.2.30 - Latent semantic analysis
Section 4.1.2.2.31 - Multifactor dimensionality reduction
