Trajectory Outlier Detection Based on Multi-Factors
Summary :
Most existing outlier detection algorithms only utilized location of trajectory points and neglected some important factors such as speed, acceleration, and corner. To address this problem, we present a Trajectory Outlier Detection algorithm based on Multi-Factors (TODMF). TODMF is improved in terms of distance-based outlier detection algorithms. It combines multi-factors into outlier detection to find more meaningful trajectory outliers. We resort to Canonical Correlation Analysis (CCA) to optimize the number of factors when determining what factors will be considered. Finally, the experiments with real trajectory data sets show that TODMF performs efficiently and effectively when applied to the problem of trajectory outlier detection.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.8 pp.2170-2173
- Publication Date
- 2014/08/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E97.D.2170
- Type of Manuscript
- LETTER
- Category
- Data Engineering, Web Information Systems
Authors
Lei ZHANG
China University of Mining and Technology
Zimu HU
China University of Mining and Technology
Guang YANG
China University of Mining and Technology
Keyword
Latest Issue
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Lei ZHANG, Zimu HU, Guang YANG, "Trajectory Outlier Detection Based on Multi-Factors" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 8, pp. 2170-2173, August 2014, doi: 10.1587/transinf.E97.D.2170.
Abstract: Most existing outlier detection algorithms only utilized location of trajectory points and neglected some important factors such as speed, acceleration, and corner. To address this problem, we present a Trajectory Outlier Detection algorithm based on Multi-Factors (TODMF). TODMF is improved in terms of distance-based outlier detection algorithms. It combines multi-factors into outlier detection to find more meaningful trajectory outliers. We resort to Canonical Correlation Analysis (CCA) to optimize the number of factors when determining what factors will be considered. Finally, the experiments with real trajectory data sets show that TODMF performs efficiently and effectively when applied to the problem of trajectory outlier detection.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E97.D.2170/_p
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@ARTICLE{e97-d_8_2170,
author={Lei ZHANG, Zimu HU, Guang YANG, },
journal={IEICE TRANSACTIONS on Information},
title={Trajectory Outlier Detection Based on Multi-Factors},
year={2014},
volume={E97-D},
number={8},
pages={2170-2173},
abstract={Most existing outlier detection algorithms only utilized location of trajectory points and neglected some important factors such as speed, acceleration, and corner. To address this problem, we present a Trajectory Outlier Detection algorithm based on Multi-Factors (TODMF). TODMF is improved in terms of distance-based outlier detection algorithms. It combines multi-factors into outlier detection to find more meaningful trajectory outliers. We resort to Canonical Correlation Analysis (CCA) to optimize the number of factors when determining what factors will be considered. Finally, the experiments with real trajectory data sets show that TODMF performs efficiently and effectively when applied to the problem of trajectory outlier detection.},
keywords={},
doi={10.1587/transinf.E97.D.2170},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Trajectory Outlier Detection Based on Multi-Factors
T2 - IEICE TRANSACTIONS on Information
SP - 2170
EP - 2173
AU - Lei ZHANG
AU - Zimu HU
AU - Guang YANG
PY - 2014
DO - 10.1587/transinf.E97.D.2170
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2014
AB - Most existing outlier detection algorithms only utilized location of trajectory points and neglected some important factors such as speed, acceleration, and corner. To address this problem, we present a Trajectory Outlier Detection algorithm based on Multi-Factors (TODMF). TODMF is improved in terms of distance-based outlier detection algorithms. It combines multi-factors into outlier detection to find more meaningful trajectory outliers. We resort to Canonical Correlation Analysis (CCA) to optimize the number of factors when determining what factors will be considered. Finally, the experiments with real trajectory data sets show that TODMF performs efficiently and effectively when applied to the problem of trajectory outlier detection.
ER -
English
