Bayesian non-negative matrix factorization with adaptive sparsity and smoothness prior

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Bayesian non-negative matrix factorization with adaptive sparsity and smoothness prior

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Bayesian non-negative matrix factorization with adaptive sparsity and smoothness prior

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GA18-07247S" target="_blank" >GA18-07247S: Metody a algoritmy pro analýzu obrazů vektorových a tenzorových polí</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Signal Processing Letters

ISSN

1070-9908

e-ISSN

—

Svazek periodika

26

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

5

Strana od-do

510-514

Kód UT WoS článku

000458852100008

EID výsledku v databázi Scopus

2-s2.0-85061747380