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Journal Articles Entropy Year : 2021

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

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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. 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 (called \texttt{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.
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Dates and versions

hal-01796011 , version 1 (18-05-2018)
hal-01796011 , version 2 (08-05-2019)

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Benjamin Guedj, Le Li. Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly. Entropy, 2021, ⟨10.3390/e23111534⟩. ⟨hal-01796011v2⟩
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