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A Note on Alternating Minimization Algorithm for the Matrix Completion Problem
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DOI：10.1109/LSP.2016.2576979.png)
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Abstract
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We consider the problem of reconstructing a low-rank matrix from a subset of its entries and analyze two variants of the so-called alternating minimization algorithm, which has been proposed in the past. We establish thatwhen the underlying matrix has rank one, has positive bounded entries, and the graph underlying the revealed entries has diameter which is logarithmic in the size of the matrix, both algorithms succeed in reconstructing the matrix approximately in polynomial time starting from an arbitrary initialization. We further provide simulation results which suggest that the second variant which is based on the message passing type updates performs significantly better.
Keywords:
Alternating minimization
compressive sensing
low-rank matrix completion
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