| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0376327 |
| utime |
20240111140815.7 |
| mtime |
20120911235959.9 |
| DOI |
10.1007/978-3-642-28551-6_37 |
| title
(primary) (eng) |
On Revealing Replicating Structures in Multiway Data: A Novel Tensor Decomposition Approach |
| specification |
| page_count |
9 s. |
| media_type |
C |
|
| serial |
| ARLID |
cav_un_epca*0376325 |
| ISBN |
978-3-642-28550-9 |
| title
|
Latent Variable Analysis and Signal Separation |
| page_num |
297-305 |
| publisher |
| place |
Heidelberg |
| name |
Springer |
| year |
2012 |
|
| editor |
|
|
| keyword |
tensor decomposition |
| keyword |
pattern analysis |
| keyword |
structural complexity |
| author
(primary) |
| ARLID |
cav_un_auth*0274170 |
| name1 |
Phan |
| name2 |
A. H. |
| country |
JP |
|
| author
|
| ARLID |
cav_un_auth*0272321 |
| name1 |
Cichocki |
| name2 |
A. |
| country |
JP |
|
| author
|
| ARLID |
cav_un_auth*0101212 |
| name1 |
Tichavský |
| name2 |
Petr |
| full_dept (cz) |
Stochastická informatika |
| full_dept |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department |
SI |
| institution |
UTIA-B |
| full_dept |
Department of Stochastic Informatics |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0208388 |
| name1 |
Mandic |
| name2 |
D. |
| country |
GB |
|
| author
|
| ARLID |
cav_un_auth*0214972 |
| name1 |
Matsuoka |
| name2 |
K. |
| country |
JP |
|
| source |
|
| cas_special |
| project |
| project_id |
GA102/09/1278 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0253174 |
|
| abstract
(eng) |
A novel tensor decomposition is proposed to make it possible to identify replicating structures in complex data, such as textures and patterns in music spectrograms. In order to establish a computational framework for this paradigm, we adopt a multiway (tensor) approach. To this end, a novel tensor product is introduced, and the subsequent analysis of its properties shows a perfect match to the task of identification of recurrent structures present in the data. Out of a whole class of possible algorithms, we illuminate those derived so as to cater for orthogonal and nonnegative patterns. Simulations on texture images and a complex music sequence confirm the benefits of the proposed model and of the associated learning algorithms. |
| action |
| ARLID |
cav_un_auth*0280771 |
| name |
Latent Variable Analysis and Signal Separation,10th International Conference, LVA/ICA 2012 |
| place |
Tel Aviv |
| dates |
12.03.2012-15.03.2012 |
| country |
IL |
|
| reportyear |
2013 |
| RIV |
BB |
| num_of_auth |
5 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0208757 |
| arlyear |
2012 |
| mrcbU56 |
533kB |
| mrcbU63 |
cav_un_epca*0376325 Latent Variable Analysis and Signal Separation 978-3-642-28550-9 297 305 Heidelberg Springer 2012 Lecture Notes on Computer Science 7191 |
| mrcbU67 |
Theis Fabian 340 |
|