Name
Abstract | ||
|---|---|---|
When confronted with massive data streams, summarizing data with dimension reduction methods such as PCA raises theoretical and algorithmic pitfalls. A principal curve acts as a nonlinear generalization of PCA, and the present paper proposes a novel algorithm to automatically and sequentially learn principal curves from data streams. We show that our procedure is supported by regret bounds with optimal sublinear remainder terms. A greedy local search implementation (called slpc, for sequential learning principal curves) that incorporates both sleeping experts and multi-armed bandit ingredients is presented, along with its regret computation and performance on synthetic and real-life data. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/e23111534 | ENTROPY |
Keywords | DocType | Volume |
sequential learning, principal curves, data streams, regret bounds, greedy algorithm, sleeping experts | Journal | 23 |
Issue | ISSN | Citations |
11 | 1099-4300 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Le Li | 1 | 0 | 0.34 |
| Benjamin Guedj | 2 | 9 | 8.82 |