Book ChapterDOI
Some Comments on Variational Bayes Block Sparse Modeling with Correlated Entries
Shruti Sharma,Santanu Chaudhury,Jayadeva +2 more
- pp 110-117
TLDR
The effect of the threshold to prune out variance parameters of algorithms corresponding to several choices of marginals, viz. multivariate Jeffery prior, multivariate Laplace distribution and multivariate Student’s t distribution is discussed.Abstract:
We present some details of Bayesian block sparse modeling using hierarchical prior having deterministic and random parameters when entries within the blocks are correlated. In particular, the effect of the threshold to prune out variance parameters of algorithms corresponding to several choices of marginals, viz. multivariate Jeffery prior, multivariate Laplace distribution and multivariate Student’s t distribution, is discussed. We also provide details of experiments with Electroencephalograph (EEG) data which shed some light on the possible applicability of the proposed Sparse Variational Bayes framework.read more
References
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Book
A Mathematical Introduction to Compressive Sensing
Simon Foucart,Holger Rauhut +1 more
TL;DR: A Mathematical Introduction to Compressive Sensing gives a detailed account of the core theory upon which the field is build and serves as a reliable resource for practitioners and researchers in these disciplines who want to acquire a careful understanding of the subject.
Journal ArticleDOI
Block-Sparse Signals: Uncertainty Relations and Efficient Recovery
TL;DR: The significance of the results presented in this paper lies in the fact that making explicit use of block-sparsity can provably yield better reconstruction properties than treating the signal as being sparse in the conventional sense, thereby ignoring the additional structure in the problem.
Journal ArticleDOI
Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs
TL;DR: A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI) that were higher than those using a widely used fast Fourier transform (FFT)-based spectrum estimation method.
Journal ArticleDOI
Extension of SBL Algorithms for the Recovery of Block Sparse Signals With Intra-Block Correlation
Zhilin Zhang,Bhaskar D. Rao +1 more
TL;DR: It is shown that exploiting intra-block correlation is very helpful in improving recovery performance, and two families of algorithms based on the framework of block sparse Bayesian learning (BSBL) are proposed to exploit such correlation and improve performance.
Journal ArticleDOI
Compressed Sensing of EEG for Wireless Telemonitoring With Low Energy Consumption and Inexpensive Hardware
TL;DR: Experimental results show that block sparse Bayesian learning is better than state-of-the-art CS algorithms, and sufficient for practical use, and suggest that BSBL is very promising for telemonitoring of EEG and other nonsparse physiological signals.
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