| bibtype |
J -
Journal Article
|
| ARLID |
0500888 |
| utime |
20240103221507.4 |
| mtime |
20190201235959.9 |
| SCOPUS |
85061747380 |
| WOS |
000458852100008 |
| DOI |
10.1109/LSP.2019.2897230 |
| title
(primary) (eng) |
Bayesian non-negative matrix factorization with adaptive sparsity and smoothness prior |
| specification |
| page_count |
5 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0253212 |
| ISSN |
1070-9908 |
| title
|
IEEE Signal Processing Letters |
| volume_id |
26 |
| volume |
3 (2019) |
| page_num |
510-514 |
| publisher |
| name |
Institute of Electrical and Electronics Engineers |
|
|
| keyword |
Non-negative matrix factorization |
| keyword |
Covariance matrix model |
| keyword |
Blind source separation |
| keyword |
Variational Bayes method |
| keyword |
Dynamic renal scintigraphy |
| author
(primary) |
| ARLID |
cav_un_auth*0267768 |
| name1 |
Tichý |
| name2 |
Ondřej |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0371641 |
| name1 |
Bódiová |
| name2 |
Lenka |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101207 |
| name1 |
Šmídl |
| name2 |
Václav |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0360229 |
| project_id |
GA18-07247S |
| agency |
GA ČR |
|
| abstract
(eng) |
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. |
| result_subspec |
WOS |
| RIV |
BB |
| FORD0 |
20000 |
| FORD1 |
20200 |
| FORD2 |
20205 |
| reportyear |
2020 |
| num_of_auth |
3 |
| mrcbC52 |
4 A hod 4ah 20231122143804.5 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0293325 |
| mrcbC64 |
1 Department of Adaptive Systems UTIA-B 10200 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS |
| confidential |
S |
| mrcbC86 |
2 Article Engineering Electrical Electronic |
| mrcbC91 |
C |
| mrcbT16-e |
ENGINEERINGELECTRICALELECTRONIC |
| mrcbT16-j |
1.106 |
| mrcbT16-s |
1.145 |
| mrcbT16-B |
81.438 |
| mrcbT16-D |
Q1 |
| mrcbT16-E |
Q4 |
| arlyear |
2019 |
| mrcbTft |
\nSoubory v repozitáři: tichy-0500888.pdf |
| mrcbU14 |
85061747380 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000458852100008 WOS |
| mrcbU63 |
cav_un_epca*0253212 IEEE Signal Processing Letters 1070-9908 1558-2361 Roč. 26 č. 3 2019 510 514 Institute of Electrical and Electronics Engineers |
|