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Parallel algorithms for tensor completion in the CP format
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2016 (English)In: Parallel Computing, ISSN 0167-8191, E-ISSN 1872-7336, Vol. 57, 222-234 p.Article in journal (Refereed) Published
Abstract [en]

Low-rank tensor completion addresses the task of filling in missing entries in multidimensional data. It has proven its versatility in numerous applications, including context aware recommender systems and multivariate function learning. To handle large-scale datasets and applications that feature high dimensions, the development of distributed algorithms is central. In this work, we propose novel, highly scalable algorithms based on a combination of the canonical polyadic (CP) tensor format with block coordinate descent methods. Although similar algorithms have been proposed for the matrix case, the case of higher dimensions gives rise to a number of new challenges and requires a different paradigm for data distribution. The convergence of our algorithms is analyzed and numerical experiments illustrate their performance on distributed-memory architectures for tensors from a range of different applications.

Place, publisher, year, edition, pages
2016. Vol. 57, 222-234 p.
National Category
Computer Science
URN: urn:nbn:se:umu:diva-126508DOI: 10.1016/j.parco.2015.10.002ISI: 000383307100017OAI: diva2:1040408
Available from: 2016-10-27 Created: 2016-10-10 Last updated: 2016-10-27Bibliographically approved

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Karlsson, Lars
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