Name
Abstract | ||
|---|---|---|
A novel approach is proposed to extract high-rank patterns from multiway data. The method is useful when signals comprise collinear components or complex structural patterns. Alternating least squares and multiplication algorithms are developed for the new model with/without non negativity constraints. Experimental results on synthetic data and real-world dataset confirm the validity of the proposed model and algorithms. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6638254 | ICASSP |
Keywords | Field | DocType |
matrix multiplication,synthetic dataset,signal processing,real-world dataset,block term decomposition,least squares algorithms,least squares approximations,paralind,rank-overlap,nonnegativity constraints,multiplication algorithms,matrix decomposition,candecomp/parafac (cp),tensor decompositions,high-rank pattern extraction,kronecker tensor decomposition (ktd),multiway data,collinear components,tensors,complex structural patterns,tensile stress,feature extraction,spectrogram,electroencephalography,face | Singular value decomposition,Signal processing,Mathematical optimization,Multiplication algorithm,Tensor,Pattern recognition,Computer science,Matrix decomposition,Synthetic data,Artificial intelligence,Non-linear least squares,Matrix multiplication | Conference |
ISSN | Citations | PageRank |
1520-6149 | 4 | 0.43 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anh Huy Phan | 1 | 828 | 51.60 |
| Andrzej Cichocki | 2 | 5228 | 508.42 |
| Petr Tichavský | 3 | 341 | 41.01 |
| Rafal Zdunek | 4 | 653 | 55.22 |
| Sidney R. Lehky | 5 | 28 | 5.30 |