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Thanks for the implementation. We have a similarity matrix W, and we were wondering if we could use SpectralNet to learn embeddings (in the spectral basis of the associated Laplacian of the similarity matrix).
It seems that W is being learned as part of the optimization procedure in SpectralNet, but we want to shortcircuit that.
The text was updated successfully, but these errors were encountered:
The easiest reference function you can look at is full_affinity(X, scale), which is used in the first part of the conditional in the referenced part of the python script.
Thanks for the implementation. We have a similarity matrix W, and we were wondering if we could use SpectralNet to learn embeddings (in the spectral basis of the associated Laplacian of the similarity matrix).
It seems that W is being learned as part of the optimization procedure in SpectralNet, but we want to shortcircuit that.
The text was updated successfully, but these errors were encountered: