Title
Nonparametric Decentralized Detection and Sparse Sensor Selection Via Weighted Kernel
Abstract
The kernel-based nonparametric approach proposed by Nguyen, Wainwright, and Jordan is further investigated for decentralized detection. In contrast with the uniform kernel used in the previous work, a weighted kernel is proposed, where weight parameters serve to selectively incorporate sensors’ information into the fusion center’s decision rule based on quality of sensors’ observations. Furthermore, weight parameters also serve as sensor selection parameters with nonzero parameters corresponding to sensors being selected. By introducing the $l_1$ regularization on weight parameters into the risk minimization framework, sensor selection is jointly performed with decision rules for sensors and the fusion center with the resulting optimal decision rule having only sparse nonzero weight parameters. A gradient projection algorithm and a Gauss-Seidel algorithm are developed to solve the risk minimization problem, which is nonconvex, and both algorithms are shown to converge to critical points. Conditions on the sample complexity to guarantee asymptotically small estimation error are characterized based on analysis of Rademacher complexity. Connection between the probability of error and the risk function is also studied. Numerical results are provided to demonstrate the advantages and properties of the proposed approach based on weighted kernel.
Year
DOI
Venue
2016
10.1109/TSP.2015.2474297
Signal Processing, IEEE Transactions
Keywords
Field
DocType
Convergence,Gauss-Seidel algorithm,KL-property,RKHS,gradient projection,non-convex problem,risk minimization,sensor selection
Decision rule,Kernel (linear algebra),Mathematical optimization,Optimal decision,Algorithm design,Rademacher complexity,Nonparametric statistics,Fusion center,Mathematics,Reproducing kernel Hilbert space
Journal
Volume
Issue
ISSN
64
2
1053-587X
Citations 
PageRank 
References 
0
0.34
20
Authors
4
Name
Order
Citations
PageRank
Weiguang Wang133.81
Yingbin Liang21646147.64
Bo Xing37332471.43
Lixin Shen443742.76