Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Discriminant Analysis with Tensor Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Beyond streams and graphs: dynamic tensor analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Least squares linear discriminant analysis
Proceedings of the 24th international conference on Machine learning
Knowledge and Information Systems
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
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Police agencies have been collecting an increasing amount of information to better understand patterns in criminal activity. Recently there is a new trend in using the data collected to predict where and when crime will occur. Crime prediction is greatly beneficial because if it is done accurately, police practitioner would be able to allocate resources to the geographic areas most at risk for criminal activity and ultimately make communities safer. In this paper, we discuss a new four-order tensor representation for crime data. The tensor encodes the longitude, latitude, time, and other relevant incidents. Using the tensor data structure, we propose the Empirical Discriminative Tensor Analysis (EDTA) algorithm to obtain sufficient discriminative information while minimizing empirical risk simultaneously. We examine the algorithm on the crime data collected in one Northeastern city. EDTA demonstrates promising results compared to other existing methods in real world scenarios.