Name
Abstract | ||
|---|---|---|
We study streaming algorithms for principal component analysis (PCA) in noisy settings. We present computationally efficient algorithms with sub-linear regret bounds for PCA in the presence of noise, missing data, and gross outliers. |
Year | Venue | Field |
|---|---|---|
2018 | ICML | Pattern recognition,Computer science,Artificial intelligence,Machine learning,Principal component analysis |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Teodor Marinov | 1 | 7 | 3.54 |
| Poorya Mianjy | 2 | 18 | 4.40 |
| R. Arora | 3 | 489 | 35.97 |