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Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow
Xiao Zhang · Simon Du · Quanquan Gu

Fri Jul 13 09:15 AM -- 12:00 PM (PDT) @ Hall B #78
We revisit the inductive matrix completion problem that aims to recover a rank-$r$ matrix with ambient dimension $d$ given $n$ features as the side prior information. The goal is to make use of the known $n$ features to reduce sample and computational complexities. We present and analyze a new gradient-based non-convex optimization algorithm that converges to the true underlying matrix at a linear rate with sample complexity only linearly depending on $n$ and logarithmically depending on $d$. To the best of our knowledge, all previous algorithms either have a quadratic dependency on the number of features in sample complexity or a sub-linear computational convergence rate. In addition, we provide experiments on both synthetic and real world data to demonstrate the effectiveness of our proposed algorithm.

Author Information

Xiao Zhang (University of Virginia)
Simon Du (Carnegie Mellon University)
Quanquan Gu (UCLA)

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