Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of entropy and mutual information
Neural Computation
Sensing and Modeling Human Networks using the Sociometer
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
The MERL motion detector dataset
Proceedings of the 2007 workshop on Massive datasets
Extracting knowledge about users' activities from raw workstation contents
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
FREEDIUS: an open source Lisp-based image understanding environment
Proceedings of the 2007 International Lisp Conference
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Analysis of massive track datasets is a challenging problem, especially when examining n-way relations inherent in social networks. In this paper, we explore ways in which stable properties of sensor observations can be extracted and visualized using a statistical sampling of features from a very large track dataset, using very little ground truth or outside knowledge. Special attention is given to methods that are likely to scale well beyond the size of the Mitsubishi dataset.