On fusion of probability density functions using tensor train decomposition
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
On fusion of probability density functions using tensor train decomposition
Original language description
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.
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
<a href="/en/project/GA22-11101S" target="_blank" >GA22-11101S: Tensor Decomposition in Active Fault Diagnosis for Stochastic Large Scale Systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2024 27th International Conference on Information Fusion (FUSION)
ISBN
978-1-73774-976-9
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
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Publisher name
IEEE
Place of publication
Venice
Event location
Venice, Italy
Event date
Jul 7, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001334560000203