More on manifold learning

I hope to present here some result in February, but I’ll expose what I’ve implemented so far :

  • Isomap
  • LLE
  • Laplacian Eigenmaps
  • Hessian Eigenmaps
  • Diffusion Maps (in fact a variation of Laplacian Eigenmaps)
  • Curvilinear Component Analysis (the reduction part)
  • NonLinear Mapping (Sammon)
  • My own technique (reduction, regression and projection)
  • PCA (usual reduction, but robust projection with an a priori term)

The results I will show here are mainly reduction comparison between the techniques, knowing that each technique has a specific field of application : LLE is not made to respect the geodesic distances, Isomap, NLM and my technique are.

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5 thoughts on “More on manifold learning

  1. Matt, I can’t wait for this package. My work occasionally touches on manifold learning and I haven’t had the guts to re-write all my matlab code for python. This will be great.

    1. Hi Andrew,

      It is available as is with scikits.learn 0.1. In the next release, it was deleted by the maintainer, but it is sitll usable.

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