SILVA, A. P. Tensor techniques in signal processing: algorithms for the canonical polyadic decomposition (PARAFAC). 2016. 124 f. Tese (Doutorado em Engenharia de Teleinformática) - Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2016. / Submitted by Marlene Sousa (mmarlene@ufc.br) on 2016-09-01T18:41:38Z
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Previous issue date: 2016-06-29 / Low rank tensor decomposition has been playing for the last years an important role in many applications
such as blind source separation, telecommunications, sensor array processing, neuroscience,
chemometrics, and data mining. The Canonical Polyadic tensor decomposition is very attractive when
compared to standard matrix-based tools, manly on system identification. In this thesis, we propose:
(i) several algorithms to compute specific low rank-approximations: finite/iterative rank-1 approximations,
iterative deflation approximations, and orthogonal tensor decompositions. (ii) A new strategy
to solve multivariate quadratic systems, where this problem is reduced to a best rank-1 tensor approximation
problem. (iii) Theoretical results to study and proof the performance or the convergence of
some algorithms. All performances are supported by numerical experiments
| Identifer | oai:union.ndltd.org:IBICT/oai:www.repositorio.ufc.br:riufc/19361 |
| Date | 29 June 2016 |
| Creators | Silva, Alex Pereira da |
| Contributors | Mota, João César Moura, Almeida, André Lima Férrer de |
| Source Sets | IBICT Brazilian ETDs |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/doctoralThesis |
| Source | reponame:Repositório Institucional da UFC, instname:Universidade Federal do Ceará, instacron:UFC |
| Rights | info:eu-repo/semantics/openAccess |
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