NDLI: Nonparametric low-rank tensor imputation

Content Provider	IEEE Xplore Digital Library
Author	Bazerque, J.A. Mateos, G. Giannakis, G.B.
Copyright Year	2012
Description	Author affiliation: Dept. of ECE, Univ. of Minnesota, Minneapolis, 55455, USA (Bazerque, J.A.; Mateos, G.; Giannakis, G.B.)
Abstract	Completion or imputation of three-way data arrays with missing entries is a basic problem encountered in various areas, including bio-informatics, image processing, and preference analysis. If available, prior information about the data at hand should be incorporated to enhance performance of the imputation method adopted. This is the motivation behind the proposed low-rank tensor estimator which leverages the correlation across slices of the data cube in the form of reproducing kernels. The rank of the tensor estimate is controlled by a novel regularization on the factors of its PARAFAC decomposition. Such a regularization is inspired by a reformulation of the nuclear norm for matrices, which allows to bypass the challenge that rank and singular values of tensors are unrelated quantities. The proposed technique is tested on MRI data of the brain with 30% missing data, resulting in a recovery error of −17dB.
Starting Page	876
Ending Page	879
File Size	196941
Page Count	4
File Format	PDF
ISBN	9781467301824
e-ISBN	9781467301817
DOI	10.1109/SSP.2012.6319847
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tensile stress Matrix decomposition Correlation Kernel Minimization Vectors Approximation methods kernel methods Tensor low-rank missing data
Content Type	Text
Resource Type	Article

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