Name
Abstract | ||
|---|---|---|
When confronted with massive data streams, summarizing data with dimension reduction methods such as PCA raises theoretical and algorithmic pitfalls. Principal curves act 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 that incorporates both sleeping experts and multi-armed bandit ingredients is presented, along with its regret bound and performance on a toy example and seismic data. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Machine Learning | Sublinear function,Mathematical optimization,Data stream mining,Dimensionality reduction,Nonlinear system,Regret,Remainder,Theoretical computer science,Local search (optimization),Sequence learning,Mathematics |
DocType | Volume | Citations |
Journal | abs/1805.07418 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin Guedj | 1 | 9 | 8.82 |
| Le Li | 2 | 6 | 1.43 |