Category:Dimension reduction

• Dimensionality reduction

A

• Autoencoder

C

• Canonical correspondence analysis
• Correspondence analysis
• Count sketch

D

• Detrended correspondence analysis

E

• Elastic map

F

• Feature selection

G

• Generalized canonical correlation
• Generalized multidimensional scaling

I

• Independent component analysis

K

• Kernel principal component analysis

L

• Local tangent space alignment
• Locality-sensitive hashing
• Low-rank approximation

M

• Modes of variation
• Multidimensional analysis
• Multidimensional scaling
• Multifactor dimensionality reduction
• Multilinear principal component analysis
• Multilinear subspace learning
• Multiple correspondence analysis

N

• NOMINATE (scaling method)
• Nonlinear dimensionality reduction

O

• Ordination (statistics)

P

• Preference regression
• Principal component analysis
• Proper generalized decomposition

R

• Random indexing
• Random projection
• Relationship square
• Relief (feature selection)
• Robust principal component analysis

S

• Sammon mapping
• Self-organizing map
• Semantic mapping (statistics)
• Semidefinite embedding
• Sliced inverse regression
• Sparse PCA
• Stress majorization
• Sufficient dimension reduction

T

• T-distributed stochastic neighbor embedding
• Tensor sketch
• Tucker decomposition

V

• Variational autoencoder