Li, Le;
Guedj, Benjamin;
(2021)
Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly.
Entropy
, 23
(11)
, Article 1534. 10.3390/e23111534.
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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.
| Type: | Article |
|---|---|
| Title: | Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly |
| Location: | Switzerland |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/e23111534 |
| Publisher version: | http://dx.doi.org/10.3390/e23111534 |
| Language: | English |
| Additional information: | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Sequential learning; principal curves; data streams; regret bounds; greedy algorithm; sleeping experts |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10186203 |
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