Name
Abstract | ||
|---|---|---|
Given an instantaneous mixture of some source signals, the blind signal separation BSS problem consists of the identification of both the mixing matrix and the original sources. By itself, it is a non-unique matrix factorization problem, while unique solutions can be obtained by imposing additional assumptions such as statistical independence. By mapping the matrix data to a tensor and by using tensor decompositions afterwards, uniqueness is ensured under certain conditions. Tensor decompositions have been studied thoroughly in literature. We discuss the matrix to tensor step and present tensorization as an important concept on itself, illustrated by a number of stochastic and deterministic tensorization techniques. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-22482-4_1 | LVA/ICA |
Keywords | Field | DocType |
Blind source separation,Independent component analysis,Tensorization,Canonical polyadic decomposition,Block term decomposition,Higher-order tensor,Multilinear algebra | Applied mathematics,Mathematical optimization,Multilinear algebra,Tensor (intrinsic definition),Tensor,Matrix (mathematics),Matrix decomposition,Independent component analysis,Tensor contraction,Blind signal separation,Mathematics | Conference |
Volume | ISSN | Citations |
9237 | 0302-9743 | 9 |
PageRank | References | Authors |
0.52 | 25 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Otto Debals | 1 | 50 | 6.55 |
| Lieven De Lathauwer | 2 | 3002 | 226.72 |