Multi-task compressive sensing with Dirichlet process priors
Proceedings of the 25th international conference on Machine learning
Compressive light transport sensing
ACM Transactions on Graphics (TOG)
Compressive sensing for multi-static scattering analysis
Journal of Computational Physics
Sparsity preserving projections with applications to face recognition
Pattern Recognition
IEEE Transactions on Signal Processing
Exploiting structure in wavelet-based Bayesian compressive sensing
IEEE Transactions on Signal Processing
Weighted l1 minimization for sparse recovery with prior information
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
Sparsity preserving discriminant analysis for single training image face recognition
Pattern Recognition Letters
An iterative Bayesian algorithm for sparse component analysis in presence of noise
IEEE Transactions on Signal Processing
Compressed sensing approach for high throughput carrier screen
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Bayesian compressive sensing via belief propagation
IEEE Transactions on Signal Processing
Variance-component based sparse signal reconstruction and model selection
IEEE Transactions on Signal Processing
Bayesian compressive sensing using Laplace priors
IEEE Transactions on Image Processing
Compressive distilled sensing: sparse recovery using adaptivity in compressive measurements
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Group testing strategies for recovery of sparse signals in noise
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Computationally efficient sparse Bayesian learning via belief propagation
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Sparse representation of medical images via compressed sensing using Gaussian scale mixtures
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Computationally efficient sparse Bayesian learning via belief propagation
IEEE Transactions on Signal Processing
Image compressed sensing based on wavelet transform in contourlet domain
Signal Processing
Bayesian compressive sensing for cluster structured sparse signals
Signal Processing
Practical data compression in wireless sensor networks: A survey
Journal of Network and Computer Applications
Single-frame image recovery using a Pearson type VII MRF
Neurocomputing
Energy-aware sparse approximation technique (EAST) for rechargeable wireless sensor networks
EWSN'10 Proceedings of the 7th European conference on Wireless Sensor Networks
Active learning framework for post-silicon variation extraction and test cost reduction
Proceedings of the International Conference on Computer-Aided Design
Block-Based Compressed Sensing of Images and Video
Foundations and Trends in Signal Processing
Dimensionality reduction via compressive sensing
Pattern Recognition Letters
Compressed sensing and Cholesky decomposition on FPGAs and GPUs
Parallel Computing
Convex feasibility modeling and projection methods for sparse signal recovery
Journal of Computational and Applied Mathematics
A wideband compressed spectrum sensing platform for dynamic spectrum access networks
Proceedings of the 18th annual international conference on Mobile computing and networking
Environmental monitoring via compressive sensing
Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data
Compressive sensing based sub-mm accuracy UWB positioning systems: A space-time approach
Digital Signal Processing
A power laws-based reconstruction approach to end-to-end network traffic
Journal of Network and Computer Applications
Compressive system identification: Sequential methods and entropy bounds
Digital Signal Processing
i-Vector with sparse representation classification for speaker verification
Speech Communication
Proceedings of the 50th Annual Design Automation Conference
A learning-based method for compressive image recovery
Journal of Visual Communication and Image Representation
Sensor selection via compressed sensing
Automatica (Journal of IFAC)
Bayesian compressive sensing as applied to directions-of-arrival estimation in planar arrays
Journal of Electrical and Computer Engineering - Special issue on Advances in Radar Technologies
Compressive sensing using the modified entropy functional
Digital Signal Processing
Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation
The Journal of Machine Learning Research
Recurrent networks for compressive sampling
Neurocomputing
Hi-index | 35.72 |
The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M Lt N of basis-function coefficients associated with B. Compressive sensing is a framework whereby one does not measure one of the aforementioned N-dimensional signals directly, but rather a set of related measurements, with the new measurements a linear combination of the original underlying N-dimensional signal. The number of required compressive-sensing measurements is typically much smaller than N, offering the potential to simplify the sensing system. Let f denote the unknown underlying N-dimensional signal, and g a vector of compressive-sensing measurements, then one may approximate f accurately by utilizing knowledge of the (under-determined) linear relationship between f and g, in addition to knowledge of the fact that f is compressible in B. In this paper we employ a Bayesian formalism for estimating the underlying signal f based on compressive-sensing measurements g. The proposed framework has the following properties: i) in addition to estimating the underlying signal f, "error bars" are also estimated, these giving a measure of confidence in the inverted signal; ii) using knowledge of the error bars, a principled means is provided for determining when a sufficient number of compressive-sensing measurements have been performed; iii) this setting lends itself naturally to a framework whereby the compressive sensing measurements are optimized adaptively and hence not determined randomly; and iv) the framework accounts for additive noise in the compressive-sensing measurements and provides an estimate of the noise variance. In this paper we present the underlying theory, an associated algorithm, example results, and provide comparisons to other compressive-sensing inversion algorithms in the literature.