Name
Abstract | ||
|---|---|---|
Robust principal component analysis (RPCA) has been widely used for many data analysis problems in matrix data. Robust tensor principal component analysis (RTPCA) aims to extract the low rank and sparse components of multidimensional data, which is a generation of RPCA. The current RTPCA methods are directly based on tensor singular value decomposition (t-SVD), which is a new tensor decomposition ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/JSTSP.2018.2873142 | IEEE Journal of Selected Topics in Signal Processing |
Keywords | Field | DocType |
Matrix decomposition,Principal component analysis,Sparse matrices,Singular value decomposition,Convex functions,Robustness,Tensors | Singular value decomposition,Mathematical optimization,Tensor,Matrix (mathematics),Computer science,Matrix decomposition,Algorithm,Robust principal component analysis,Matrix norm,Principal component analysis,Sparse matrix | Journal |
Volume | Issue | ISSN |
12 | 6 | 1932-4553 |
Citations | PageRank | References |
9 | 0.46 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yipeng Liu | 1 | 117 | 26.05 |
| Longxi Chen | 2 | 27 | 2.34 |
| Ce Zhu | 3 | 1473 | 117.79 |