matlab 降维工具箱

Matlab Toolbox for Dimensionality Reduction
 
降维方法包括:
  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 (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. • 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)

     

posted on 2014-09-25 10:12  alexanderkun  阅读(2347)  评论(0编辑  收藏  举报

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