Classification with Low Rank and Missing Data
Machine Learning
2015-01-15 v1
Abstract
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 kernels) classify provably as well as the best classifier that has access to the full data.
Cite
@article{arxiv.1501.03273,
title = {Classification with Low Rank and Missing Data},
author = {Elad Hazan and Roi Livni and Yishay Mansour},
journal= {arXiv preprint arXiv:1501.03273},
year = {2015}
}