Flexible Low-Rank Statistical Modeling with Missing Data and Side Information
We explore a general statistical framework for low-rank modeling of matrix-valued data, based on convex optimization with a generalized nuclear norm penalty. We study several related problems: the usual low-rank matrix completion problem with flexible loss functions arising from generalized linear m...
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| Format: | Article |
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Institute of Mathematical Statistics
2019
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| Online Access: | http://hdl.handle.net/1721.1/120549 https://orcid.org/0000-0003-1384-9743 |