Manifold Learning with High Dimensional Model Representations

Gulsen Taskin, Gustau Camps-Valls

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

3 Atıf (Scopus)

Özet

Manifold learning methods are very efficient methods for hyperspectral image (HSI) analysis but, unless specifically designed, they cannot provide an explicit embedding map readily applicable to out-of-sample data. A common assumption to deal with the problem is that the transformation between the high input dimensional space and the (typically low) latent space is linear. This is a particularly strong assumption, especially when dealing with hyperspectral images due to the well-known nonlinear nature of the data. To address this problem, a manifold learning method based on High Dimensional Model Representation (HDMR) is proposed, which enables to present a nonlinear embedding function to project out-of-sample samples into the latent space. The proposed method is compared to its linear counterparts and achieves promising performance in terms of classification accuracy of hyperspectral images.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
Sayfalar1675-1678
Sayfa sayısı4
ISBN (Elektronik)9781728163741
DOI'lar
Yayın durumuYayınlandı - 26 Eyl 2020
Etkinlik2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States
Süre: 26 Eyl 20202 Eki 2020

Yayın serisi

AdıInternational Geoscience and Remote Sensing Symposium (IGARSS)

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???event.eventtypes.event.conference???2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Ülke/BölgeUnited States
ŞehirVirtual, Waikoloa
Periyot26/09/202/10/20

Bibliyografik not

Publisher Copyright:
© 2020 IEEE.

Finansman

FinansörlerFinansör numarası
Horizon 2020 Framework Programme647423

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