How to classify data into N classes + one "garbage" class (or leave some data out)?
I would recommend to 

make a binary classifier that classifies into
"garbage"/"non-garbage" classes
make a regular N-classes classifier that classifies "non-garbage"
inputs
I am trying to classify images of cells into N classes ("cell 1", "cell 2" ...) but some of the images are just noise and I would like to either not classify them or put them into a "garbage" class.
I have tried the latter, but it is not very successful and I suspect it is because my "garbage" class is very heterogenous.
Any suggestions on how to allow excluding some data from classification, or classify it as noise?
I am using python and sklearn, but I would welcome either specific python/sklearn tips or generic machine learning algorithms.