Exploiting Optimal Supports in Enhanced Multivariance Products Representation for Lossy Compression of Hyperspectral Images

M. Enis Şen*, Süha Tuna

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Özet

Thanks to advancements in technology, the importance of computational methods used in tasks like storing and processing data is increasing as the data produced becomes more complex in both size and detail. Methods such as Tucker Decomposition, CANDECOMP/PARAFAC, Alternating Least Squares and their derivations, are widely used in the field to meet the requirements in numerous areas. These cases contain expressing high-dimensional data using lower-dimensional tensors, cleansing the data of errors that occur during data acquisition while also ensuring an efficient compression. This study proposes a new method that exploits the tensor structure of 3-dimensional data by calculating the lower-dimensional components via Enhanced Multivariance Products Representation and produces a superior approximation compared to well-known tensor decomposition methods. An iterative process is established to calculate the optimal support tensors and to determine the lower-dimensional components, which in further steps are employed to reconstruct the approximation.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9798350360493
DOI'lar
Yayın durumuYayınlandı - 2023
Etkinlik14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Virtual, Bursa, Turkey
Süre: 30 Kas 20232 Ara 2023

Yayın serisi

Adı14th International Conference on Electrical and Electronics Engineering, ELECO 2023 - Proceedings

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???event.eventtypes.event.conference???14th International Conference on Electrical and Electronics Engineering, ELECO 2023
Ülke/BölgeTurkey
ŞehirVirtual, Bursa
Periyot30/11/232/12/23

Bibliyografik not

Publisher Copyright:
© 2023 IEEE.

Finansman

ACKNOWLEDGMENT Computing resources used in this work were provided by the National Center for High Performance Computing of Türkiye (UHeM) under grant number 1016472023.

FinansörlerFinansör numarası
National Center for High Performance Computing of Türkiye
Ulusal Yüksek Başarımlı Hesaplama Merkezi, Istanbul Teknik Üniversitesi1016472023

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