Online matrix completionOnline variational BayesIncomplete dataSparse subspace learningLow-rankExtracting the underlying low-dimensional space where high-dimensional signals often reside has been at the center of numerous algorithms in the signal processing and machine learning literature during the past few ...
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Recently, another group of methods stems from the emerging theory of low-rank matrix completion, which is derived from compressed sensing theory [12], were proposed to accomplish the accurate restoration from the whole 3D dataset effectively. Low-rank matrix completion technology relies on self-simil...
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Low-rank matrix recovery (LRMR) has been becoming an increasingly popular technique for analyzing data with missing entries, gross corruptions, and outliers. As a significant component of LRMR, the...