Hi authors,
Thank you for your excellent work.
I have a quick question regarding the implementation of the polynomial MMD kernel.
-
In the code, the mmd_poly function appears to use the sklearn default for gamma, which is None (scaled to 1 / n_features).
videojedi/mmd_polynomial.py#L6
videojedi/JEDi.py#L19
-
In the paper's appendix, it is stated that gamma is set to 1.0.
-
My own test suggests the code with the default gamma=None produces the metric scale reported in the paper (~0.5). When I explicitly set gamma=1.0, the MMD value increases to the order of 1e3.
This leads me to believe the implementation with gamma=None was used for the paper's results. Could you please clarify which is the correct implementation corresponding to the paper's findings?
Thank you for your time and help!

Hi authors,
Thank you for your excellent work.
I have a quick question regarding the implementation of the polynomial MMD kernel.
In the code, the mmd_poly function appears to use the sklearn default for gamma, which is None (scaled to 1 / n_features).
videojedi/mmd_polynomial.py#L6
videojedi/JEDi.py#L19
In the paper's appendix, it is stated that gamma is set to 1.0.
My own test suggests the code with the default gamma=None produces the metric scale reported in the paper (~0.5). When I explicitly set gamma=1.0, the MMD value increases to the order of 1e3.
This leads me to believe the implementation with gamma=None was used for the paper's results. Could you please clarify which is the correct implementation corresponding to the paper's findings?
Thank you for your time and help!