Sparse audio representations using the MCLT
Signal Processing - Sparse approximations in signal and image processing
Digital Signal Processing
Nonlinear underdetermined blind signal separation using Bayesian neural network approach
Digital Signal Processing
Improved FOCUSS method with conjugate gradient iterations
IEEE Transactions on Signal Processing
A neural network pruning approach based on compressive sampling
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Strong sub-and super-gaussianity
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Efficient Sensing Topology Management for Spatial Monitoring with Sensor Networks
Journal of Signal Processing Systems
Direct data domain STAP using sparse representation of clutter spectrum
Signal Processing
Robust ISAR imaging based on compressive sensing from noisy measurements
Signal Processing
Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
SIAM Journal on Imaging Sciences
Sidelobe Suppression for Robust Beamformer Via the Mixed Norm Constraint
Wireless Personal Communications: An International Journal
Multi-resolutive sparse approximations of d-dimensional data
Computer Vision and Image Understanding
Multiple task learning using iteratively reweighted least square
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We develop robust methods for subset selection based on the minimization of diversity measures. A Bayesian framework is used to account for noise in the data and a maximum a posteriori (MAP) estimation procedure leads to an iterative procedure which is a regularized version of the focal underdetermined system solver (FOCUSS) algorithm. The convergence of the regularized FOCUSS algorithm is established and it is shown that the stable fixed points of the algorithm are sparse. We investigate three different criteria for choosing the regularization parameter: quality of fit; sparsity criterion; L-curve. The L-curve method, as applied to the problem of subset selection, is found not to be robust, and we propose a novel modified L-curve procedure that solves this problem. Each of the regularized FOCUSS algorithms is evaluated through simulation of a detection problem, and the results are compared with those obtained using a sequential forward selection algorithm termed orthogonal matching pursuit (OMP). In each case, the regularized FOCUSS algorithm is shown to be superior to the OMP in noisy environments.