I don't fully understand the difference between these two smoothing algorithms. It seems like they both take a window, fit a polynomial, sample from the fit, and move on.
I would guess maybe the difference lies in the weighting function used by LOESS but not by the Savitzky-Golay filter, but I'm not sure exactly how this works or what the ultimate effects on the fit would be.
I'm particularly interested in the differences and relative advantages/disadvantages to each for fitting data that is not evenly sampled. I understand there's a generalized Savitzky-Golay filter that works for non-evenly sampled data, albeit much less efficiently.