Predictive Low Rank Matrix Learning under Partial Observations: Mixed-Projection ADMM
\(\) We study the problem of learning a partially observed matrix under the low rank assumption in the presence of fully observed side information that depends linearly on the true underlying matrix. This problem consists of an important generalization of the Matrix Completion problem, a central problem in Statistics, Operations Research and Machine Learning, that … Read more