TY - GEN
T1 - Manifold Learning with High Dimensional Model Representations
AU - Taskin, Gulsen
AU - Camps-Valls, Gustau
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/26
Y1 - 2020/9/26
N2 - 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.
AB - 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.
KW - Dimensionality reduction
KW - HDMR
KW - high dimensional model representation
KW - hyperspectral image classification
KW - manifold learning
UR - http://www.scopus.com/inward/record.url?scp=85101975103&partnerID=8YFLogxK
U2 - 10.1109/IGARSS39084.2020.9324215
DO - 10.1109/IGARSS39084.2020.9324215
M3 - Conference contribution
AN - SCOPUS:85101975103
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1675
EP - 1678
BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Y2 - 26 September 2020 through 2 October 2020
ER -