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Award ID contains: 1741342

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  1. ; ; (, Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  2. ; ; (, International Conference on Machine Learning)
    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. 
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    Accepted Manuscript Full Text Available