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Classification with Low Rank and Missing Data

Author(s): Hazan, Elad; Livni, Roi; Mansour, Yishayc

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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.
Publication Date: 2015
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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