Institutional Repository
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI: | http://purl.tuc.gr/dl/dias/6CBF8B5D-0DF3-4C71-AA32-133D89222E65 | ||
| Year | 2019 | ||
| Type of Item | Conference Full Paper | ||
| License |
|
||
| Bibliographic Citation | K. Makantasis, A. Voulodimos, A. Doulamis, N. Doulamis and I. Georgoulas, "Hyperspectral image classification with tensor-based rank-R learning models," in 26th IEEE International Conference on Image Processing, 2019, pp. 3148-3152. doi: 10.1109/ICIP.2019.8803268 https://doi.org/10.1109/ICIP.2019.8803268 | ||
| Appears in Collections |
In this paper, we present a general tensor-based nonlinear classifier, the Rank-R Feedforward Neural Network (FNN). In the proposed model, which is an extension of the Rank-1 FNN classifier, the network weights are constrained to satisfy a rank-R Canonical Polyadic Decomposition. By allowing a rank-R, instead of a rank-1, Canonical Polyadic Decomposition of the weights, the learning capacity of the model can be increased, which contributes to avoiding underfitting problems. The effectiveness of the proposed model is scrutinized on a hyperspectral image classification experimental setting, since hyperspectral data can naturally be represented as tensor objects. Performance evaluation results indicate that the proposed model outperforms other state-of-the-art models, including deep learning ones, especially in cases where the number of available training samples is small.
Copyright © DIAS 2013
