MATLAB Dimensionality Reduction toolbox

MATLAB toolbox for dimensionality loss ction dimensionality reduction methods include:

1. Principal Component Analysis (PCA)

2. • Probabilistic PCA

3. • Factor Analysis (FA)

4. • Sammon Mapping

5. • Linear discriminant analysis (LDA)

6. • Multidimensional Scaling (MDS)

7. • Isomap

8. • Landmark Isomap

9. • Local linear embedding (LLE)

10. • Laplacian eigenmaps

11. • Hessian lle

12. • Local tangent space alignment (ltsa)

13. • Conformal eigenmaps (extension of LLE)

14. • Maximum variance unfolding (extension of LLE)

15. • Landmark mvu (landmarkmvu)

16. • Fast maximum variance unfolding (faw.vu)

17. • Kernel PCA

18. • Generalized discriminant analysis (GDA)

19. • Diffusion maps

20. • Neighborhood preserving embedding (NPE)

21. • Locality preserving projection (LPP)

22. • Linear local tangent space alignment (lltsa)

23. • Stochastic proximity embedding (SPE)

24. • Multilayer autoencoders (training by RBM + Backpropagation or by an evolutionary algorithm)

25. • Local linear coordination (LLC)

26. • Manifold charting

27. • Coordinated Factor Analysis (CFA)

28. • Gaussian process latent variable model (gplvm)

29. • Stochastic neighbor embedding (SNE)

30. • Symmetric SNE (symsne)

31. • New: T-distributed stochastic neighbor embedding (t-SNE)

32. • New: neighborhood Components Analysis (NCA)

33. • New: maximally collapsing metric Learning (MCML)

    : Http://homepage.tudelft.nl/19j49/Matlab_Toolbox_for_Dimensionality_Reduction.html

MATLAB Dimensionality Reduction toolbox