Obiekt

Tytuł: Times series averaging and denoising from a probabilistic perspective on time-elastic kernels

Autor:

Marteau, Pierre-Francois

Data wydania:

2019

Typ zasobu:

artykuł

Współtwórca:

Kobusińska, Anna - ed. ; Hsu, Ching-Hsien - ed. ; Lin, Kwei-Jay - ed.

Podtytuł:

.

Tytuł publikacji grupowej:

AMCS, volume 29 (2019)

Abstract:

In the light of regularized dynamic time warping kernels, this paper re-considers the concept of a time elastic centroid for a set of time series. We derive a new algorithm based on a probabilistic interpretation of kernel alignment matrices. This algorithm expresses the averaging process in terms of stochastic alignment "automata". It uses an iterative agglomerative heuristic method for averaging the aligned samples, while also averaging the times of their occurrence. ; By comparing classification accuracies for 45 heterogeneous time series data sets obtained by first nearest centroid/medoid classifiers, we show that (i) centroid-based approaches significantly outperform medoid-based ones, (ii) for the data sets considered, our algorithm, which combines averaging in the sample space and along the time axes, emerges as the most significantly robust model for time-elastic averaging with a promising noise reduction capability. We also demonstrate its benefit in an isolated gesture recognition experiment and its ability to significantly reduce the size of training instance sets. Finally, we highlight its denoising capability using demonstrative synthetic data. Specifically, we show that it is possible to retrieve, from few noisy instances, a signal whose components are scattered in a wide spectral band.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Identyfikator zasobu:

oai:zbc.uz.zgora.pl:85981

DOI:

10.2478/amcs-2019-0028

Strony:

375-392

Źródło:

AMCS, volume 29, number 2 (2019) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego

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