Manifold Learning with High Dimensional Model Representations

Gulsen Taskin, Gustau Camps-Valls

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781728163741
Publication statusPublished - 26 Sept 2020
Event2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States
Duration: 26 Sept 20202 Oct 2020

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)


Conference2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Country/TerritoryUnited States
CityVirtual, Waikoloa

Bibliographical note

Publisher Copyright:
© 2020 IEEE.


FundersFunder number
Horizon 2020 Framework Programme647423


    • Dimensionality reduction
    • HDMR
    • high dimensional model representation
    • hyperspectral image classification
    • manifold learning


    Dive into the research topics of 'Manifold Learning with High Dimensional Model Representations'. Together they form a unique fingerprint.

    Cite this