On fusion of probability density functions using tensor train decomposition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43973067" target="_blank" >RIV/49777513:23520/24:43973067 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.23919/FUSION59988.2024.10706475" target="_blank" >https://doi.org/10.23919/FUSION59988.2024.10706475</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/FUSION59988.2024.10706475" target="_blank" >10.23919/FUSION59988.2024.10706475</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On fusion of probability density functions using tensor train decomposition

Popis výsledku v původním jazyce

Non-linear filters consider probability density functions in various non-parametric representations. They often suffer from the curse of dimensionality. Computation of weights over a grid of points becomes infeasible even for low dimensions. Filters processing data produced in different sensor nodes provide their own probability densities. Combination of such densities is desired. A favourite paradigm is to construct a fused density as a weighted arithmetic or geometric mean of the individual densities. This paper prospects the fusion for tensor train representation of densities produced by point-mass filters. In this representation, the weights are neither evaluated for a whole grid nor fully stored in the memory of the filters. Aspects of tensor-train-based fusion are discussed, such as computation of auxiliary characteristics and experience with numerical examples.

Název v anglickém jazyce

On fusion of probability density functions using tensor train decomposition

Popis výsledku anglicky

Non-linear filters consider probability density functions in various non-parametric representations. They often suffer from the curse of dimensionality. Computation of weights over a grid of points becomes infeasible even for low dimensions. Filters processing data produced in different sensor nodes provide their own probability densities. Combination of such densities is desired. A favourite paradigm is to construct a fused density as a weighted arithmetic or geometric mean of the individual densities. This paper prospects the fusion for tensor train representation of densities produced by point-mass filters. In this representation, the weights are neither evaluated for a whole grid nor fully stored in the memory of the filters. Aspects of tensor-train-based fusion are discussed, such as computation of auxiliary characteristics and experience with numerical examples.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GA22-11101S" target="_blank" >GA22-11101S: Tenzorový rozklad v aktivní diagnostice poruch pro stochastické rozlehlé systémy</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2024 27th International Conference on Information Fusion (FUSION)

ISBN

978-1-73774-976-9

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Venice

Místo konání akce

Venice, Italy

Datum konání akce

7. 7. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001334560000203