Name
Abstract | ||
|---|---|---|
We propose novel tensor decomposition methods that advocate both properties of sparsity and robustness to outliers. The sparsity enables us to extract some essential features from a big data that are easily interpretable. The robustness ensures the resistance to outliers that appear commonly in high-dimensional data. We first propose a method that generalizes the ridge regression in M-estimation framework for tensor decompositions. The other approach we propose combines the least absolute deviation (LAD) regression and the least absolute shrinkage operator (LASSO) for the CANDECOMP/PARAFAC (CP) tensor decompositions. We also formulate various robust tensor decomposition methods using different loss functions. The simulation study shows that our robust-sparse methods outperform other general tensor decomposition methods in the presence of outliers. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/GlobalSIP.2013.6737053 | IEEE Global Conference on Signal and Information Processing |
Keywords | Field | DocType |
tensors,regression analysis | Mathematical optimization,Tensor,Regression,Regression analysis,Lasso (statistics),Outlier,Algorithm,Robustness (computer science),Least absolute deviations,Operator (computer programming),Mathematics | Conference |
ISSN | Citations | PageRank |
2376-4066 | 2 | 0.42 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyon-Jung Kim | 1 | 12 | 2.48 |
| Esa Ollila | 2 | 351 | 33.51 |
| Visa Koivunen | 3 | 1917 | 187.81 |
| Christophe Croux | 4 | 205 | 27.51 |