Hinging Hyperplanes for Time-Series Segmentation

Abstract

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.

Spatiotemporal Data Mining Problems and Methods

“…In addition to the area, there is a difference in the time of data download in the above methods. The FMRI receives data every 2 s, while the EEG every 1 ms [54]. The combination of data obtained from the above data acquisition methods is the final form of data that is analyzed in order to map the human brain in specific activities and to extract the required standards.…”