Comments (3)
Sure, PR is quite welcome. I think the better design for custom kernel would be similar to how KernelPCA fit
handles different kernels, see here: https://github.com/JuliaStats/MultivariateStats.jl/blob/99ee965df3a8e136ff2d0fcb10456b434e1f9001/src/kpca.jl#L157-L165
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thx, wanted to change this for our research project anyway so glad I could try to help out. let me know if you want me to change anything or have improvements you want me to make.
[we are using it for when the gram matrix is from a graph kernel so the X
input argument does not work for us.]
https://github.com/wildart/ManifoldLearning.jl/pull/28/files
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This is included in the new release.
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Related Issues (20)
- Tag a new version? HOT 14
- Fit / transform interface for embeddings HOT 2
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