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IPSN
2004
Springer
15 years 7 months ago
Estimation from lossy sensor data: jump linear modeling and Kalman filtering
Due to constraints in cost, power, and communication, losses often arise in large sensor networks. The sensor can be modeled as an output of a linear stochastic system with random...
Alyson K. Fletcher, Sundeep Rangan, Vivek K. Goyal
TSP
2012
13 years 10 months ago
Distributed Covariance Estimation in Gaussian Graphical Models
—We consider distributed estimation of the inverse covariance matrix in Gaussian graphical models. These models factorize the multivariate distribution and allow for efficient d...
Ami Wiesel, Alfred O. Hero
102
Voted
GI
2010
Springer
15 years 29 days ago
Extended Object and Group Tracking: A Comparison of Random Matrices and Random Hypersurface Models
: Based on previous work of the authors, this paper provides a comparison of two different tracking methodologies for extended objects and group targets, where the true shape of th...
Marcus Baum, Michael Feldmann, Dietrich Fraenken, ...
159
Voted
SDM
2009
SIAM
202views Data Mining» more  SDM 2009»
15 years 11 months ago
Proximity-Based Anomaly Detection Using Sparse Structure Learning.
We consider the task of performing anomaly detection in highly noisy multivariate data. In many applications involving real-valued time-series data, such as physical sensor data a...
Tsuyoshi Idé, Aurelie C. Lozano, Naoki Abe,...