Canonical polyadic tensor decomposition with low-rank factor matrices

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00542514" target="_blank" >RIV/67985556:_____/21:00542514 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICASSP39728.2021.9414606" target="_blank" >http://dx.doi.org/10.1109/ICASSP39728.2021.9414606</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICASSP39728.2021.9414606" target="_blank" >10.1109/ICASSP39728.2021.9414606</a>

Result language
angličtina
Original language name
Canonical polyadic tensor decomposition with low-rank factor matrices
Original language description
This paper proposes a constrained canonical polyadic (CP) tensor decomposition method with low-rank factor matrices. In this way, we allow the CP decomposition with high rank while keeping the number of the model parameters small. First, we propose an algorithm to decompose the tensors into factor matrices of given ranks. Second, we propose an algorithm which can determine the ranks of the factor matrices automatically, such that the fitting error is bounded by a user- selected constant. The algorithms are verified on the decomposition of a tensor of the MNIST hand-written image dataset.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ISBN
978-1-7281-7605-5
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
4690-4694
Publisher name
IEEE
Place of publication
Piscataway
Event location
Toronto
Event date
Jun 6, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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