| Full text | |
| Author(s): |
da Costa, Michele N.
;
Lopes, Renato R.
;
Romano, Joao Marcos T.
;
IEEE
Total Authors: 4
|
| Document type: | Journal article |
| Source: | 2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO); v. N/A, p. 5-pg., 2016-01-01. |
| Abstract | |
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) | |
| FAPESP's process: | 14/23936-4 - Applications of multidimensional data processing using tensor methods |
| Grantee: | Michele Nazareth da Costa |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |