Parallel algorithms for tensor completion in the CP format

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. (C) 2015 Elsevier B.V. All rights reserved.


Published in:
Parallel Computing, 57, 222-234
Presented at:
8th International Workshop on Parallel Matrix Algorithms and Applications (PMAA), Univ Svizzera Italiana, Lugano, SWITZERLAND, JUL 02-04, 2014
Year:
2016
Publisher:
Amsterdam, Elsevier Science Bv
ISSN:
0167-8191
Keywords:
Laboratories:




 Record created 2016-10-18, last modified 2018-09-13


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