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Pregled bibliografske jedinice broj: 637706

Hinging Hyperplanes for Time-Series Segmentation


Huang, Xiaolin; Matijaš, Marin; Suykens, Johan A.K.
Hinging Hyperplanes for Time-Series Segmentation // IEEE Transactions on Neural Networks and Learning Systems, 24 (2013), 8; 1279-1291 doi:10.1109/TNNLS.2013.2254720 (međunarodna recenzija, članak, znanstveni)


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Naslov
Hinging Hyperplanes for Time-Series Segmentation

Autori
Huang, Xiaolin ; Matijaš, Marin ; Suykens, Johan A.K.

Izvornik
IEEE Transactions on Neural Networks and Learning Systems (2162-237X) 24 (2013), 8; 1279-1291

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Hinging hyperplanes; lasso; least squares support vector machine; segmentation; time series

Sažetak
Division of a time series into segments is a common technique for time-series processing, and is known as segmentation. Segmentation is traditionally done by linear interpolation in order to guarantee the continuity of the reconstructed time series. The interpolation-based segmentation methods may perform poorly for data with a level of noise because interpolation is noise sensitive. To handle the problem, this paper establishes an explicit expression for segmentation from a compact representation for piecewise linear functions using hinging hyperplanes. This expression enables the use of regression to obtain a continuous reconstructed signal and, as a consequence, application of advanced techniques in segmentation. In this paper, a least squares support vector machine with lasso using a hinging feature map is given and analyzed, based on which a segmentation algorithm and its online version are established. Numerical experiments conducted on synthetic and real-world datasets demonstrate the advantages of our methods compared to existing segmentation algorithms.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Elektrotehnika, Računarstvo



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Marin Matijaš (autor)

Citiraj ovu publikaciju

Huang, Xiaolin; Matijaš, Marin; Suykens, Johan A.K.
Hinging Hyperplanes for Time-Series Segmentation // IEEE Transactions on Neural Networks and Learning Systems, 24 (2013), 8; 1279-1291 doi:10.1109/TNNLS.2013.2254720 (međunarodna recenzija, članak, znanstveni)
Huang, X., Matijaš, M. & Suykens, J. (2013) Hinging Hyperplanes for Time-Series Segmentation. IEEE Transactions on Neural Networks and Learning Systems, 24 (8), 1279-1291 doi:10.1109/TNNLS.2013.2254720.
@article{article, year = {2013}, pages = {1279-1291}, DOI = {10.1109/TNNLS.2013.2254720}, keywords = {Hinging hyperplanes, lasso, least squares support vector machine, segmentation, time series}, journal = {IEEE Transactions on Neural Networks and Learning Systems}, doi = {10.1109/TNNLS.2013.2254720}, volume = {24}, number = {8}, issn = {2162-237X}, title = {Hinging Hyperplanes for Time-Series Segmentation}, keyword = {Hinging hyperplanes, lasso, least squares support vector machine, segmentation, time series} }
@article{article, year = {2013}, pages = {1279-1291}, DOI = {10.1109/TNNLS.2013.2254720}, keywords = {Hinging hyperplanes, lasso, least squares support vector machine, segmentation, time series}, journal = {IEEE Transactions on Neural Networks and Learning Systems}, doi = {10.1109/TNNLS.2013.2254720}, volume = {24}, number = {8}, issn = {2162-237X}, title = {Hinging Hyperplanes for Time-Series Segmentation}, keyword = {Hinging hyperplanes, lasso, least squares support vector machine, segmentation, time series} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


Uključenost u ostale bibliografske baze podataka:


  • IEEE Xplore, Scopus


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