Kernel Based Online Change Point Detection
| Autor: | Ikram Bouchikhi, A. Ferrari, Marc Bernot, Cedric Richard, Anthony Bourrier |
|---|---|
| Přispěvatelé: | Joseph Louis LAGRANGE (LAGRANGE), Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Observatoire de la Côte d'Azur, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Data stream mining 020206 networking & telecommunications 02 engineering and technology Function (mathematics) [SDU.ASTR.IM]Sciences of the Universe [physics]/Astrophysics [astro-ph]/Instrumentation and Methods for Astrophysic [astro-ph.IM] [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] Kernel (statistics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing State (computer science) Time series Null hypothesis Algorithm [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing Change detection ComputingMilieux_MISCELLANEOUS Reproducing kernel Hilbert space |
| Zdroj: | 2019 27th European Signal Processing Conference (EUSIPCO) 2019 27th European Signal Processing Conference (EUSIPCO), Sep 2019, A Coruna, Spain. pp.1-5, ⟨10.23919/EUSIPCO.2019.8902582⟩ EUSIPCO |
| DOI: | 10.23919/EUSIPCO.2019.8902582⟩ |
| Popis: | Detecting change points in time series data is a challenging problem, in particular when no prior information on the data distribution and the nature of the change is available. In a former work, we introduced an online non-parametric change-point detection framework built upon direct density ratio estimation over two consecutive time segments, rather than modeling densities separately. This algorithm based on the theory of reproducing kernels showed positive and reliable detection results for a variety of problems. To further improve the detection performance of this approach, we propose in this paper to modify the original cost function in order to achieve unbiasedness of the density ratio estimation under the null hypothesis. Theoretical analysis and numerical simulations confirm the improved behavior of this method, as well as its efficiency compared to a state of the art one. Application to sentiment change detection in Twitter data streams is also presented. |
| Databáze: | OpenAIRE |
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