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Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors
2015
IEEE Transactions on Signal Processing
Extracting latent low-dimensional structure from high-dimensional data is of paramount importance in timely inference tasks encountered with 'Big Data' analytics. However, increasingly noisy, heterogeneous, and incomplete datasets as well as the need for real-time processing of streaming data pose major challenges to this end. In this context, the present paper permeates benefits from rank minimization to scalable imputation of missing data, via tracking low-dimensional subspaces and unraveling
doi:10.1109/tsp.2015.2417491
fatcat:4upze7gda5dubngompfntmmc6y