
Targeted matrix completion
Matrix completion is a problem that arises in many dataanalysis setting...
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Matrix Completion on Graphs
The problem of finding the missing values of a matrix given a few of its...
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SweetRS: Dataset for a recommender systems of sweets
Benchmarking recommender system and matrix completion algorithms could b...
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TopN Recommender System via Matrix Completion
TopN recommender systems have been investigated widely both in industry...
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On the Fundamental Limits of Matrix Completion: Leveraging Hierarchical Similarity Graphs
We study the matrix completion problem that leverages hierarchical simil...
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Cluster Developing 1Bit Matrix Completion
Matrix completion has a longtime history of usage as the core technique...
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A divideandconquer algorithm for binary matrix completion
We propose an algorithm for low rank matrix completion for matrices with...
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Mixture Matrix Completion
Completing a data matrix X has become an ubiquitous problem in modern data science, with applications in recommender systems, computer vision, and networks inference, to name a few. One typical assumption is that X is lowrank. A more general model assumes that each column of X corresponds to one of several lowrank matrices. This paper generalizes these models to what we call mixture matrix completion (MMC): the case where each entry of X corresponds to one of several lowrank matrices. MMC is a more accurate model for recommender systems, and brings more flexibility to other completion and clustering problems. We make four fundamental contributions about this new model. First, we show that MMC is theoretically possible (wellposed). Second, we give its precise informationtheoretic identifiability conditions. Third, we derive the sample complexity of MMC. Finally, we give a practical algorithm for MMC with performance comparable to the stateoftheart for simpler related problems, both on synthetic and real data.
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