
















Suppose that we want to cluster data in
a way that



guarantees that
we still have a good model even if an



adversary
removes one of the cluster centers from our



model.



Estep: find the
two cluster centers that are closest to



each data point.
Each of these cluster centers is given a


responsibility of
0.5 for that datapoint.



Mstep:
Reestimate each cluster center to be the mean



of the datapoints
it is responsible for.




Proof that it
converges:




The
Estep optimizes F subject to the constraint that



the
distribution contains 0.5 in two places.




The
Mstep optimizes F with the distribution fixed

