January 15th 2008 05:27 pm
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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3 Responses to “More on manifold learning”
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Barry Wark on 16 Jan 2008 at 5:04 pm #
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.
Matt on 17 Jan 2008 at 9:29 am #
I hope to be able to propose the code in the near future. Stay tuned
Acomplia on 16 Feb 2008 at 6:47 pm #
Thanks for sharing!