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.

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