Bayesian non-negative matrix factorization with adaptive sparsity and smoothness prior
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F19%3A00500888" target="_blank" >RIV/67985556:_____/19:00500888 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/8633424" target="_blank" >https://ieeexplore.ieee.org/document/8633424</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/LSP.2019.2897230" target="_blank" >10.1109/LSP.2019.2897230</a>
Alternative languages
Result language
angličtina
Original language name
Bayesian non-negative matrix factorization with adaptive sparsity and smoothness prior
Original language description
Non-negative matrix factorization (NMF) is generally an ill-posed problem which requires further regularization. Regularization of NMF using the assumption of sparsity is common as well as regularization using smoothness. In many applications it is natural to assume that both of these assumptions hold together. To avoid ad hoc combination of these assumptions using weighting coefficient, we formulate the problem using a probabilistic model and estimate it in a Bayesian way. Specifically, we use the fact that the assumptions of sparsity and smoothness are different forms of prior covariance matrix modeling. We use a generalized model that includes both sparsity and smoothness as special cases and estimate all its parameters using the variational Bayes method. The resulting matrix factorization algorithm is compared with state-of-the-art algorithms on large clinical dataset of 196 image sequences from dynamic renal scintigraphy. The proposed algorithm outperforms other algorithms in statistical evaluation.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/GA18-07247S" target="_blank" >GA18-07247S: Methods and Algorithms for Vector and Tensor Field Image Analysis</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Signal Processing Letters
ISSN
1070-9908
e-ISSN
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Volume of the periodical
26
Issue of the periodical within the volume
3
Country of publishing house
US - UNITED STATES
Number of pages
5
Pages from-to
510-514
UT code for WoS article
000458852100008
EID of the result in the Scopus database
2-s2.0-85061747380