SVM

[mateuszbaran]

I've noticed that you (@kellertuer ) have written "SVM". Do you actually mean providing a set of RKHS kernels like in that paper: https://arxiv.org/pdf/1412.0265.pdf ?

EDIT: I just realized that I forgot to mention that I meant SVM from the https://github.com/JuliaManifolds/ManifoldML.jl/blob/master/ideas.md file.

[kellertuer]

I would like to first agree on a framework to use, haven't had time to look at a few (maybe MLJ?)

But first of all I then want to do the simpler ones: Geodesic Regression for example or MDM – but yes in the long run SVM would be nice to have, too, for sure.

[mateuszbaran]

I'm not sure we actually need to be exclusive to one particular framework. MLJ integration would be nice for sure but so far I haven't used that package. I've looked at it briefly and I don't really know how to approach that. Somehow we would have to specify that certain data comes from a certain manifold so we'd probably need a whole new family of scientific types: https://github.com/alan-turing-institute/MLJScientificTypes.jl ? I would prefer to figure it out later unless there is a clear need for a framework.

[kellertuer]

Still, I would like to provide algorithms in this package in a unified way (maybe model, fit, predict), so either we would have to model that ourselves or stich to an existing framework, I think.

[mateuszbaran]

fit and predict exist at least in StatsBase.jl and MLJModelInterface.jl.

[mateuszbaran]

By the way, MDM is essentially kNN for k=1 so PR #1 sort of already covers it.