Is there a way to include a spatial regularization penalty to cost functions in scikit-learn for clustering?
More specifically, I am working with neuroscience brain data, where every voxels has a spatially inherited dependency based on their proximity. Using 2-classes gaussian mixture learning, I would like to obtain, for each voxel, a probability score of being labeled as '1' vs '0' (based on 30-ish samples). However this task is pointless if I cannot include a regularization based on neighborhood, as voxels are not completely independents.