Choosing number of dimensions in LLE

Question

Is there a way to choose the number of reduced dimensions d where d<D and D is the number of original dimensions in the original dataset? I'm trying to use the LLE technique by using this code in python and I would like to find a way to choose n_components:

embed_lle = LLE(n_neighbors=5)
result = embed_lle.fit_transform(features)

This paper has instructed the following 'The LLE method [2] is based on the assumption that d is known.'

In PCA, we can use the explained_variance_ratio_ method along with the elbow method to find the optimal number of components. But this does not seem to be the case for LLE. Is there a way to choose the reduced number of dimensions?

Any help would be highly appreciated!