preprint · arXiv (Cornell University) · 2012

The Annealing Sparse Bayesian Learning Algorithm

Benyuan Liu, Hongqi Fan, Zaiqi Lu, Qiang Fu

Cited by 0Open access

Abstract

In this paper we propose a two-level hierarchical Bayesian model and an annealing schedule to re-enable the noise variance learning capability of the fast marginalized Sparse Bayesian Learning Algorithms. The performance such as NMSE and F-measure can be greatly improved due to the annealing technique. This algorithm tends to produce the most sparse solution under moderate SNR scenarios and can outperform most concurrent SBL algorithms while pertains small computational load.

Cite this paper

APA 7

Liu, B., Fan, H., Lu, Z., & Fu, Q. (2012). The Annealing Sparse Bayesian Learning Algorithm. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1209.1033

MLA 9

Liu, Benyuan, et al. “The Annealing Sparse Bayesian Learning Algorithm.” arXiv (Cornell University), 2012, https://doi.org/10.48550/arxiv.1209.1033.

Chicago

Liu, Benyuan, Hongqi Fan, Zaiqi Lu, and Qiang Fu. 2012. “The Annealing Sparse Bayesian Learning Algorithm.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.1209.1033.

Harvard

Liu, B. et al. (2012) ‘The Annealing Sparse Bayesian Learning Algorithm’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.1209.1033.

BibTeX

@misc{liu2012annealing,
  title = {{The Annealing Sparse Bayesian Learning Algorithm}},
  author = {Liu, Benyuan and Fan, Hongqi and Lu, Zaiqi and Fu, Qiang},
  journal = {arXiv (Cornell University)},
  year = {2012},
  doi = {10.48550/arxiv.1209.1033}
}

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