Dimensionality Reduction - leemik3/tensorflow-2.0 GitHub Wiki
Linear
- PCA (Principal Component Analysis)
- LDA (Linear Discriminant Analysis)
Non-Linear
Nonlinear Dimensionality Reduction
= Representaion learning
= Efficient coding learning
= Feature Extraction
= Manifold Learning
- Autoencoders (AE)
- t-SNE (t-distributed stochastic neighbor embedding)
- Isomap
- LLE (Locally-linear embedding)
Reference
tistory
Youtube : naver d2, "μ€ν μΈμ½λμ λͺ¨λ κ² 1/3"