7.7 Dimension Reduction Method

7.7.1 Random Projection

7.7.2 Discriminant Analysis (LDA)

LDA, QDA, ODA (Oriented Discriminant Analysis) https://kilthub.cmu.edu/articles/journal_contribution/Oriented_Discriminant_Analysis_ODA_/6557243?file=12039518

Partial Least Squares Discriminant Analysis (PLS-DA)

also can do classification

7.7.3 SVD (Singular Value Decomposition)

7.7.4 Principal Component Analysis (PCA)

https://www.r-bloggers.com/2022/11/pca-for-categorical-variables-in-r/

https://en.wikipedia.org/wiki/Kosambi%E2%80%93Karhunen%E2%80%93Lo%C3%A8ve_theorem

kernel PCA

Multilinear Principal Component Analysis，MPCA https://en.wikipedia.org/wiki/Multilinear_principal_component_analysis

7.7.5 Nonnegative Matrix Factorization (NMF)

7.7.6 t-SNE

7.7.7 Locally Linear Embedding

7.7.8 Independent Component Analysis (ICA)

7.7.9 Autoencoders

7.7.10 Laplacian Eigenmaps

7.7.11 ISOMAP

7.7.12 Uniform Manifold Approximation and Projection (UMAP)

7.7.13 Self-organizing map

https://en.wikipedia.org/wiki/Self-organizing_map

7.7.14 Dynamic mode decomposition

https://en.wikipedia.org/wiki/Dynamic_mode_decomposition