Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
A Statistical Method for Profiling Network Traffic
Proceedings of the Workshop on Intrusion Detection and Network Monitoring
Relative Expected Instantaneous Loss Bounds
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Predicting Rare Events In Temporal Domains
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A Sense of Self for Unix Processes
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Image Spaces and Video Trajectories: Using Isomap to Explore Video Sequences
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Bootstrapping a data mining intrusion detection system
Proceedings of the 2003 ACM symposium on Applied computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Minority report in fraud detection: classification of skewed data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A spatio-temporal extension to Isomap nonlinear dimension reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Manifold learning visualization of network traffic data
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
Locally Linear Embedding for Markerless Human Motion Capture Using Multiple Cameras
DICTA '05 Proceedings of the Digital Image Computing on Techniques and Applications
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Semi-supervised nonlinear dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Clustering with Bregman Divergences
The Journal of Machine Learning Research
Mining irregularities in maritime container itineraries
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Anomaly detection on ITS data via view association
Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description
| Hi-index | 0.00 |
The formation of secure transportation corridors, where cargoes and shipments from points of entry can be dispatched safely to highly sensitive and secure locations, is a high national priority. One of the key tasks of the program is the detection of anomalous cargo based on sensor readings in truck weigh stations. Due to the high variability, dimensionality, and/or noise content of sensor data in transportation corridors, appropriate feature representation is crucial to the success of anomaly detection methods in this domain. In this paper, we empirically investigate the usefulness of manifold embedding methods for feature representation in anomaly detection problems in the domain of transportation corridors. We focus on both linear methods, such as multi-dimensional scaling (MDS), as well as nonlinear methods, such as locally linear embedding (LLE) and isometric feature mapping (ISOMAP). Our study indicates that such embedding methods provide a natural mechanism for keeping anomalous points away from the dense/normal regions in the embedding of the data. We illustrate the efficacy of manifold embedding methods for anomaly detection through experiments on simulated data as well as real truck data from weigh stations.