Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue Entropy Année : 2021

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

Résumé

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.
Fichier principal
Vignette du fichier
main-pcurves.pdf (1.71 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

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

Identifiants

Citer

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

Altmetric

Partager

Gmail Facebook X LinkedIn More