Matlab Toolbox for Dimensionality Reduction (v0.8.1 - March 2013)

The Matlab Toolbox for Dimensionality Reduction contains Matlab implementations of 34 techniques for dimensionality reduction and metric learning. A large number of implementations was developed from scratch, whereas other implementations are improved versions of software that was already available on the Web. The implementations in the toolbox are conservative in their use of memory. The toolbox is available for download here.

Currently, the Matlab Toolbox for Dimensionality Reduction contains the following techniques:

1. • Principal Component Analysis (PCA)

2. • Probabilistic PCA

3. • Factor Analysis (FA)

4. • Classical multidimensional scaling (MDS)

5. • Sammon mapping

6. • Linear Discriminant Analysis (LDA)

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 (FastMVU)

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. • Deep autoencoders (using denoising autoencoder pretraining)

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

31. • t-Distributed Stochastic Neighbor Embedding (t-SNE)

32. • Neighborhood Components Analysis (NCA)

33. • Maximally Collapsing Metric Learning (MCML)

34. • Large-Margin Nearest Neighbor (LMNN)