کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
566239 1451937 2017 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Sparse Bayesian dictionary learning with a Gaussian hierarchical model
ترجمه فارسی عنوان
فرهنگ لغت بیزی پراکنده یادگیری با مدل سلسله مراتبی گاوسی ☆
کلمات کلیدی
یادگیری واژه نامه. گاوسی معکوس گاما قبل؛ تغییرات بیزی؛ نمونهگیری گیبس
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی

We consider a dictionary learning problem aimed at designing a dictionary such that the signals admit a sparse or an approximate sparse representation over the learnt dictionary. The problem finds a variety of applications including image denoising, feature extraction, etc. In this paper, we propose a new hierarchical Bayesian model for dictionary learning, in which a Gaussian-inverse Gamma hierarchical prior is used to promote the sparsity of the representation. Suitable non-informative priors are also placed on the dictionary and the noise variance such that they can be reliably estimated from the data. Based on the hierarchical model, a variational Bayesian method and a Gibbs sampling method are developed for Bayesian inference. The proposed methods have the advantage that they do not require the knowledge of the noise variance a priori. Numerical results show that the proposed methods are able to learn the dictionary with an accuracy better than existing methods, particularly for the case where there is a limited number of training signals.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 130, January 2017, Pages 93–104
نویسندگان
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