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

Benjamin Guedj; Le Li

doi:10.3390/e23111534

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arXiv · 1805.07418

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

Benjamin Guedj·Le Li

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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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BibTeX RIS

Benjamin Guedj, Le Li. 2019-05-08. Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly. https://doi.org/10.3390/e23111534

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