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Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly

Benjamin Guedj 1 Le Li 2
1 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, CERIM - Santé publique : épidémiologie et qualité des soins-EA 2694, Polytech Lille - École polytechnique universitaire de Lille
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.
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Submitted on : Friday, May 18, 2018 - 9:54:16 PM
Last modification on : Tuesday, May 28, 2019 - 4:16:05 PM
Document(s) archivé(s) le : Monday, September 24, 2018 - 11:51:19 AM


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  • HAL Id : hal-01796011, version 1


Benjamin Guedj, Le Li. Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly. 2018. ⟨hal-01796011v1⟩



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