Name
Abstract | ||
|---|---|---|
L1-norm principal-component analysis (L1-PCA) is known to attain sturdy resistance against faulty points (outliers) among the processed data. However, computing the L1-PCA of large datasets, with high number of measurements and/or dimensions, may be computationally impractical; in such cases, incremental solutions could be preferred. At the same time, in many applications it is desired to track th... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/JSTSP.2018.2874165 | IEEE Journal of Selected Topics in Signal Processing |
Keywords | Field | DocType |
Principal component analysis,Signal processing algorithms,Robustness,Algorithm design and analysis | Computer vision,Algorithm design,Subspace topology,Computer science,Algorithm,Outlier,Robustness (computer science),Artificial intelligence,Signal subspace,Principal component analysis,Signal processing algorithms | Journal |
Volume | Issue | ISSN |
12 | 6 | 1932-4553 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Panos P. Markopoulos | 1 | 45 | 9.35 |
| Mayur Dhanaraj | 2 | 0 | 0.34 |
| Andreas Savakis | 3 | 377 | 41.10 |