| bibtype |
M -
Monography Chapter
|
| ARLID |
0504124 |
| utime |
20240111141018.4 |
| mtime |
20190423235959.9 |
| SCOPUS |
85065472276 |
| WOS |
000493283300034 |
| DOI |
10.1007/978-3-030-11292-9_34 |
| title
(primary) (eng) |
Practical Initialization of Recursive Mixture-Based Clustering for Non-negative Data |
| specification |
| page_count |
19 s. |
| book_pages |
812 |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0504123 |
| ISBN |
978-3-030-11292-9 |
| title
|
Informatics in Control, Automation and Robotics. ICINCO 2017. |
| part_title |
495 |
| page_num |
679-698 |
| publisher |
| place |
Cham |
| name |
Springer |
| year |
2020 |
|
| editor |
|
| editor |
|
|
| keyword |
Mixture-based clustering |
| keyword |
Recursive mixture estimation |
| keyword |
Different components |
| keyword |
Non-negative data |
| keyword |
Bayesian estimation |
| author
(primary) |
| ARLID |
cav_un_auth*0355927 |
| name1 |
Suzdaleva |
| name2 |
Evženie |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování signálů |
| full_dept (eng) |
Department of Signal Processing |
| department (cz) |
ZS |
| department (eng) |
ZS |
| full_dept |
Department of Signal Processing |
| country |
RU |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101167 |
| name1 |
Nagy |
| name2 |
Ivan |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování signálů |
| full_dept |
Department of Signal Processing |
| department (cz) |
ZS |
| department |
ZS |
| full_dept |
Department of Signal Processing |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA15-03564S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0321440 |
|
| abstract
(eng) |
The paper provides a practical guide on initialization of the recursive mixture-based clustering of non-negative data. For modeling the non-negative data, mixtures of uniform, exponential, gamma and other distributions can be used. Initialization is known to be an important task for a start of the mixture estimation algorithm. Within the considered recursive approach, the key point of initialization is a choice of initial statistics of the involved prior distributions. The paper describes several initialization techniques for the mentioned types of components that can be beneficial primarily from a practical point of view. |
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2021 |
| num_of_auth |
2 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0295982 |
| confidential |
S |
| mrcbC86 |
3+4 Proceedings Paper Automation Control Systems|Computer Science Theory Methods|Engineering Electrical Electronic|Robotics |
| arlyear |
2020 |
| mrcbU14 |
85065472276 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000493283300034 WOS |
| mrcbU56 |
pdf |
| mrcbU63 |
cav_un_epca*0504123 Informatics in Control, Automation and Robotics. ICINCO 2017. 978-3-030-11292-9 679 698 Cham Springer 2020 Lecture Notes in Electrical Engineering 495 |
| mrcbU67 |
340 Gusikhin O. |
| mrcbU67 |
340 Madani K. |
|