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dc.contributor.authorZhang, L.-
dc.contributor.authorWei, W.-
dc.contributor.authorShi, Q.-
dc.contributor.authorShen, C.-
dc.contributor.authorvan den Hengel, A.-
dc.contributor.authorZhang, Y.-
dc.identifier.citationIEEE Transactions on Neural Networks and Learning Systems, 2020; 31(1):4170-4184-
dc.descriptionDate of publication December 30, 2019; date of current version October 6, 2020-
dc.description.abstractLow-rank representation-based approaches that assume low-rank tensors and exploit their low-rank structure with appropriate prior models have underpinned much of the recent progress in tensor completion. However, real tensor data only approximately comply with the low-rank requirement in most cases, viz., the tensor consists of low-rank (e.g., principle part) as well as non-low-rank (e.g., details) structures, which limit the completion accuracy of these approaches. To address this problem, we propose an adaptive low-rank representation model for tensor completion that represents low-rank and non-low-rank structures of a latent tensor separately in a Bayesian framework. Specifically, we reformulate the CANDECOMP/PARAFAC (CP) tensor rank and develop a sparsity-induced prior for the low-rank structure that can be used to determine tensor rank automatically. Then, the non-low-rank structure is modeled using a mixture of Gaussians prior that is shown to be sufficiently flexible and powerful to inform the completion process for a variety of real tensor data. With these two priors, we develop a Bayesian minimum mean-squared error estimate framework for inference. The developed framework can capture the important distinctions between low-rank and non-low-rank structures, thereby enabling more accurate model, and ultimately, completion. For various applications, compared with the state-of-the-art methods, the proposed model yields more accurate completion results.-
dc.description.statementofresponsibilityLei Zhang, Wei Wei, Qinfeng Shi, Chunhua Shen, Anton van den Hengel, and Yanning Zhang-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.rights© 2019 IEEE-
dc.subjectAdaptive low-rank representation; automatic tensor rank determination; tensor completion-
dc.titleAccurate tensor completion via adaptive low-rank representation-
dc.typeJournal article-
dc.identifier.orcidShi, Q. [0000-0002-9126-2107]-
dc.identifier.orcidvan den Hengel, A. [0000-0003-3027-8364]-
Appears in Collections:Aurora harvest 8
Australian Institute for Machine Learning publications
Computer Science publications

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