Aspects of density approximation by tensor trains
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F25%3A43976498" target="_blank" >RIV/49777513:23520/25:43976498 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.23919/FUSION65864.2025.11124077" target="_blank" >https://doi.org/10.23919/FUSION65864.2025.11124077</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/FUSION65864.2025.11124077" target="_blank" >10.23919/FUSION65864.2025.11124077</a>
Alternative languages
Result language
angličtina
Original language name
Aspects of density approximation by tensor trains
Original language description
Point-mass filters solve Bayesian recursive relations by approximating probability density functions of a system state over grids of discrete points. The approach suffers from the curse of dimensionality. The exponential increase of the number of the grid points can be mitigated by application of low-rank approximations of multidimensional arrays. Tensor train decompositions represent individual values by the product of matrices. This paper focuses on selected issues that are substantial in state estimation. Namely, the contamination of the density approximations by negative values is discussed first. Functional decompositions of quadratic functions are compared with decompositions of discretised Gaussian densities next. In particular, the connection of correlation with tensor train ranks is explored. Last, the consequences of interpolating the density values from one grid to a new grid are analysed.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 28th International Conference on Information Fusion (FUSION)
ISBN
978-1-03-705623-9
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
1-8
Publisher name
IEEE
Place of publication
Rio de Janiero
Event location
Rio de Janiero, Brazílie
Event date
Jul 7, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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