The SVD and reduced rank signal processing
Signal Processing - Theme issue on singular value decomposition
Applied numerical linear algebra
Applied numerical linear algebra
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Distributed Karhunen-Loève Transform with nested subspaces
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Principal component analysis in decomposable Gaussian graphical models
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Wireless sensor networks for personal health monitoring: Issues and an implementation
Computer Communications
IEEE Transactions on Information Theory
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
Distributed Estimation Using Reduced-Dimensionality Sensor Observations
IEEE Transactions on Signal Processing
Relative Karhunen-Loeve transform
IEEE Transactions on Signal Processing
The Distributed Karhunen–Loève Transform
IEEE Transactions on Information Theory
Dimensionality Reduction for Distributed Estimation in the Infinite Dimensional Regime
IEEE Transactions on Information Theory
IEEE Communications Magazine
Practical data compression in wireless sensor networks: A survey
Journal of Network and Computer Applications
Hi-index | 35.68 |
In the distributed linear source coding problem, a set of distributed sensors observe subsets of a data vector with noise, and provide the fusion center linearly encoded data. The goal is to determine the encoding matrix of each sensor such that the fusion center can reconstruct the entire data vector with minimum mean square error. The recently proposed local Karhunen-Loève transform approach performs this task by optimally determining the encoding matrix of each sensor assuming the other matrices are fixed. This approach is implemented iteratively until convergence is reached. Herein, we propose a greedy algorithm. In each step, one of the encoding matrices is updated by appending an additional row. The algorithm selects in a greedy fashion a single sensor that provides the largest improvement in minimizing the mean square error. This algorithm terminates after a finite number of steps, that is, when all the encoding matrices reach their predefined encoded data size. We show that the algorithm can be implemented recursively, and compared to the iterative approach, the algorithm reduces the computational load from cubic dependency to quadratic dependency on the data size. This makes it a prime candidate for on-line and real-time implementations of the distributed Karhunen-Loève transform. Simulation results suggest that the mean square error performance of the suggested algorithm is equivalent to the iterative approach.