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Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors by Mardani, Morteza; Mateos, Gonzalo; Giannakis, Georgios B. is a scholarly article available to read on EtoBox.

What is Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors about?

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 {\em 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 latent (possibly multi-way) structure from \emph{incomplete streaming} data. For low-rank matrix data, a subspace estimator is proposed based on an exponentially-weighted least-squares criterion regularized with the nuclear norm. After recasting the non-separable nuclear norm into a form amenable to online optimization, real-time algorithms with complementary strengths are developed and their convergence is established under simplifying technical assumptions. In a stationary setting, the asymptotic estimates obtained offer the well-documented performance guarantees of the {\em batch} nuclear-norm regularized estimator. Under the same unifying framework, a novel online (adaptive) algori

Author

Mardani, Morteza; Mateos, Gonzalo; Giannakis, Georgios B.

Published

2014

Language

EN