Classification with Low Rank and Missing Data

@inproceedings{Hazan2015ClassificationWL,
  title={Classification with Low Rank and Missing Data},
  author={Elad Hazan and Roi Livni and Yishay Mansour},
  booktitle={ICML},
  year={2015}
}
We consider classification and regression tasks where we have missing data and assume that the (clean) data resides in a low rank subspace. Finding a hidden subspace is known to be computationally hard. Nevertheless, using a non-proper formulation we give an efficient agnostic algorithm that classifies as good as the best linear classifier coupled with the best low-dimensional subspace in which the data resides. A direct implication is that our algorithm can linearly (and non-linearly through… CONTINUE READING

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