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|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.|
|Citation:||Hazan, Elad, Roi Livni, and Yishay Mansour. "Classification with low rank and missing data." In Proceedings of the 32nd International Conference on Machine Learning 37 (2015): pp. 257-266.|
|Pages:||257 - 266|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Proceedings of the 32nd International Conference on Machine Learning|
|Version:||Final published version. Article is made available in OAR by the publisher's permission or policy.|
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