Hinging Hyperplanes for Time-Series Segmentation (CROSBI ID 194524)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Huang, Xiaolin ; Matijaš, Marin ; Suykens, Johan A.K.
engleski
Hinging Hyperplanes for Time-Series Segmentation
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.
Hinging hyperplanes; lasso; least squares support vector machine; segmentation; time series
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Podaci o izdanju
24 (8)
2013.
1279-1291
objavljeno
2162-237X
10.1109/TNNLS.2013.2254720
Povezanost rada
Elektrotehnika, Računarstvo, Matematika