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Spectrum Estimation from a Few Entries

2019
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Journal of machine learning research
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Singular values of a data in a matrix form provide insights on the structure of the data, the effective dimensionality, and the choice of hyper-parameters on higher-level data analysis tools. However, in many practical applications such as collaborative filtering and network analysis, we only get a partial observation. Under such scenarios, we consider the fundamental problem of recovering spectral properties of the underlying matrix from a sampling of its entries. In this paper, we address the

dblp:journals/jmlr/KhetanO19
fatcat:hk3v4qfecfh53axtkutuyqxybm