Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
IEEE Transactions on Signal Processing
Exploiting structure in wavelet-based Bayesian compressive sensing
IEEE Transactions on Signal Processing
Sampling theorems for signals from the union of finite-dimensional linear subspaces
IEEE Transactions on Information Theory
Bayesian orthogonal component analysis for sparse representation
IEEE Transactions on Signal Processing
Block-sparse signals: uncertainty relations and efficient recovery
IEEE Transactions on Signal Processing
Model-based compressive sensing
IEEE Transactions on Information Theory
Bayesian compressive sensing using Laplace priors
IEEE Transactions on Image Processing
A hierarchical Bayesian model for frame representation
IEEE Transactions on Signal Processing
Audio Denoising by Time-Frequency Block Thresholding
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
A Bayesian Approach for Blind Separation of Sparse Sources
IEEE Transactions on Audio, Speech, and Language Processing
Sparse Linear Regression With Structured Priors and Application to Denoising of Musical Audio
IEEE Transactions on Audio, Speech, and Language Processing
Maximum likelihood detection and estimation of Bernoulli - Gaussian processes
IEEE Transactions on Information Theory
Decoding by linear programming
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
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In traditional framework of compressive sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. Other than sparse prior, structures on the sparse pattern of the signal have also been used as an additional prior, called model-based compressive sensing, such as clustered structure and tree structure on wavelet coefficients. In this paper, the cluster structured sparse signals are investigated. Under the framework of Bayesian compressive sensing, a hierarchical Bayesian model is employed to model both the sparse prior and cluster prior, then Markov Chain Monte Carlo (MCMC) sampling is implemented for the inference. Unlike the state-of-the-art algorithms which are also taking into account the cluster prior, the proposed algorithm solves the inverse problem automatically-prior information on the number of clusters and the size of each cluster is unknown. The experimental results show that the proposed algorithm outperforms many state-of-the-art algorithms.