Laplacian Prior Variational Automatic Relevance Determination for Transmission Tomography

Jingwei Lu; David G. Politte; Joseph O'Sullivan

Laplacian Prior Variational Automatic Relevance Determination for Transmission Tomography

Jingwei Lu, David G. Politte, Joseph O'Sullivan

University of Missouri-St. Louis

Research output: Contribution to journal › Article › peer-review

Original language	American English
Journal	Machine Learning
State	Published - Oct 26 2017

Disciplines

Physical Sciences and Mathematics

Cite this

@article{706e3021b3df4184b6f6e123ab6bb6bf,

title = "Laplacian Prior Variational Automatic Relevance Determination for Transmission Tomography",

author = "Jingwei Lu and Politte, \{David G.\} and Joseph O'Sullivan",

note = "In the classic sparsity-driven problems, the fundamental L-1 penalty method has been shown to have good performance in reconstructing signals for a wide range of problems. However this performance relies on a good choice of penalty weight which is often found from empirical experiments.",

year = "2017",

month = oct,

day = "26",

language = "American English",

journal = "Machine Learning",

}

TY - JOUR

T1 - Laplacian Prior Variational Automatic Relevance Determination for Transmission Tomography

AU - Lu, Jingwei

AU - Politte, David G.

AU - O'Sullivan, Joseph

N1 - In the classic sparsity-driven problems, the fundamental L-1 penalty method has been shown to have good performance in reconstructing signals for a wide range of problems. However this performance relies on a good choice of penalty weight which is often found from empirical experiments.

PY - 2017/10/26

Y1 - 2017/10/26

UR - http://arxiv.org/abs/1710.09522

M3 - Article

JO - Machine Learning

JF - Machine Learning

ER -

Laplacian Prior Variational Automatic Relevance Determination for Transmission Tomography

Disciplines

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Cite this