| Texto completo | |
| Autor(es): |
da Costa, Michele N.
;
Lopes, Renato R.
;
Romano, Joao Marcos T.
;
IEEE
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO); v. N/A, p. 5-pg., 2016-01-01. |
| Resumo | |
This paper presents an algorithm for signal subspace separation in the context of multidimensional data. The proposal is an extension of the randomized Singular Value Decomposition (SVD) for higher-order tensors. From a set derived from random sampling, we construct an orthogonal basis associated with the range of each mode-space of the input data tensor. Multilinear projection of the input data onto each mode-space then transforms the data to a low-dimensional representation. Finally, we compute the Higher-Order Singular Value Decomposition (HOSVD) of the reduced tensor. Furthermore, we propose an algorithm for computing the randomized HOSVD based on the row-extraction technique. The results reveal a relevant improvement from the standpoint of computational complexity. (AU) | |
| Processo FAPESP: | 14/23936-4 - Aplicações de processamento de dados multidimensionais usando métodos tensoriais |
| Beneficiário: | Michele Nazareth da Costa |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |