Subspace Methods That Are Resistant to a Limited Number of Features Corrupted by an Adversary

Chris Mesterharm, Rauf Izmailov, Scott Alexander +1

In this paper, we consider batch supervised learning where an adversary is allowed to corrupt instances with arbitrarily large noise. The adversary is allowed to corrupt any $l$ fe…