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book part or chapter

Mixture Models for the Analysis, Edition, and Synthesis of Continuous Time Series

Calinon, S.
Bouguila, N.
•
Fan, W.
2019
Mixture Models and Applications

This chapter presents an overview of techniques used for the analysis, edition, and synthesis of time series, with a particular emphasis on motion data. The use of mixture models allows the decomposition of time signals as a superposition of basis functions. It provides a compact representation that aims at keeping the essential characteristics of the signals. Various types of basis functions have been proposed, with developments originating from different fields of research, including computer graphics, human motion science, robotics, control, and neuroscience. Examples of applications with radial, Bernstein and Fourier basis functions will be presented, with associated source codes to get familiar with these techniques.

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Type
book part or chapter
DOI
10.1007/978-3-030-23876-6_3
Author(s)
Calinon, S.
Editors
Bouguila, N.
•
Fan, W.
Date Issued

2019

Publisher

Springer

Published in
Mixture Models and Applications
Start page

39

End page

57

Series title/Series vol.

Unsupervised and Semi-Supervised Learning

Written at

EPFL

EPFL units
LIDIAP  
Available on Infoscience
February 18, 2020
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/166312
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