AAAI Publications, Twenty-Seventh AAAI Conference on Artificial Intelligence

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Robust Discrete Matrix Completion
Jin Huang, Feiping Nie, Heng Huang

Last modified: 2013-06-30


Most existing matrix completion methods seek the matrix global structure in the real number domain and produce predictions that are inappropriate for applications retaining discrete structure, where an additional step is required to post-process prediction results with either heuristic threshold parameters or complicated mappings. Such an ad-hoc process is inefficient and impractical. In this paper, we propose a novel robust discrete matrix completion algorithm that produces the prediction from the collection of user specified discrete values by introducing a new discrete constraint to the matrix completion model. Our method achieves a high prediction accuracy, very close to the most optimal value of competitive methods with threshold values tuning. We solve the difficult integer programming problem via incorporating augmented Lagrangian method in an elegant way, which greatly accelerates the converge process of our method and provides the asymptotic convergence in theory. The proposed discrete matrix completion model is applied to solve three real-world applications, and all empirical results demonstrate the effectiveness of our method.


Discrete Matrix Completion; Social Network Link Prediction; SNP Missing Value Inference; Protein Interaction Prediction

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