Name
Abstract | ||
|---|---|---|
To ensure interpretability of extracted sources in tensor decomposition, we introduce in this paper a dictionary-based tensor canonical polyadic decomposition, which enforces one factor to belong exactly to a known dictionary. A new formulation of sparse coding is proposed, which enables high-dimensional tensors dictionary-based canonical polyadic decomposition. The benefits of using a dictionary ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TSP.2017.2777393 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Tensile stress,Dictionaries,Source separation,Data models,Encoding,Indexes,Matrix decomposition | Interpretability,Data modeling,Algebra,Tensor,Identifiability,Neural coding,Matrix decomposition,Hyperspectral imaging,Mathematics,Source separation | Journal |
Volume | Issue | ISSN |
66 | 7 | 1053-587X |
Citations | PageRank | References |
1 | 0.41 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeremy E. Cohen | 1 | 46 | 8.34 |
| Nicolas Gillis | 2 | 503 | 39.77 |