NDLI: Sparse Bayesian learning using approximate message passing

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2014 48th Asilomar Conference on Signals, Systems and Computers

Flexible selective parallel algorithms for big data optimization

Fast and robust bootstrap in analysing large multivariate datasets

Online censoring for large-scale regressions

Single-trial identification of failed memory retrieval

Utilization of temporal trial dependency in ERP based BCIs

On the feasibility of fully wireless remote control for underwater vehicles

On physical layer secrecy of collaborative compressive detection

Converse results for secrecy generation over channels

Robust transmission over wiretap channels with secret keys

Helper-assisted asymmetric two key generation

Image classification by multi-kernel dictionary learning

Crowdsourced study of subjective image quality

Structured sparse representation with low-rank interference

A reduced-complexity iterative scheme for decoding quasi-cyclic low-density parity-check codes

Efficient adaptive list successive cancellation decoder for polar codes

Decoder diversity architectures for finite alphabet iterative decoders

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Concurrent exploration of MIMO radar and co-prime array for faster and higher resolution scanning

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Classification of human viewers using high-resolution EEG with SVM

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Effective connectivity in FMRI from mutual prediction approach

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Universal sequential outlier hypothesis testing

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Random linear packet coding: Joint power and rate control

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Online optimal power flow with renewables

An angular sampling theorem for the usable frequency range of antenna array calibration measurements

Partial expansion of dataflow graphs for resource-aware scheduling of multicore signal processing systems

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Decentralized regression with asynchronous sub-Nyquist sampling

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Sparse Bayesian learning using approximate message passing

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Sparse Bayesian learning using approximate message passing

Content Provider	IEEE Xplore Digital Library
Author	Al-Shoukairi, M. Rao, B.
Copyright Year	2014
Description	Author affiliation: Dept. of ECE, Univ. of California, San Diego, La Jolla, CA, USA (Al-Shoukairi, M.; Rao, B.)
Abstract	We use the approximate message passing framework (AMP) [1] to address the problem of recovering a sparse vector from undersampled noisy measurements. We propose an algorithm based on Sparse Bayesian learning (SBL) [2]. Unlike the original EM based SBL that requires matrix inversions, the proposed algorithm has linear complexity, which makes it well suited for large scale problems. Compared to other message passing techniques, the algorithm requires fewer approximations, due to the conditional Gaussian prior assumption on the original vector. Numerical results show that the proposed algorithm has comparable and in many cases better performance than existing algorithms despite significant reduction in complexity.
Starting Page	1957
Ending Page	1961
File Size	569546
Page Count	5
File Format	PDF
e-ISBN	9781479982974
DOI	10.1109/ACSSC.2014.7094812
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-11-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Approximation algorithms Complexity theory Signal processing algorithms Bayes methods Approximation methods Message passing Brain modeling
Content Type	Text
Resource Type	Article

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