Ιδρυματικό Αποθετήριο
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Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/6CBF8B5D-0DF3-4C71-AA32-133D89222E65 | - |
| Αναγνωριστικό | https://doi.org/10.1109/ICIP.2019.8803268 | - |
| Αναγνωριστικό | https://ieeexplore.ieee.org/document/8803268 | - |
| Γλώσσα | en | - |
| Μέγεθος | 5 pages | en |
| Τίτλος | Hyperspectral image classification with tensor-based rank-R learning models | en |
| Δημιουργός | Makantasis Konstantinos | en |
| Δημιουργός | Μακαντασης Κωνσταντινος | el |
| Δημιουργός | Voulodimos, Athanasios | en |
| Δημιουργός | Doulamis, Anastasios | en |
| Δημιουργός | Doulamis Nikolaos D. | en |
| Δημιουργός | Georgoulas Ioannis | en |
| Εκδότης | Institute of Electrical and Electronics Engineers | en |
| Περίληψη | 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. | en |
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Τύπος | Conference Full Paper | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2020-04-23 | - |
| Ημερομηνία Δημοσίευσης | 2019 | - |
| Θεματική Κατηγορία | Hyperspectral image classification | en |
| Θεματική Κατηγορία | Machine learning | en |
| Θεματική Κατηγορία | Tensor rank decomposition | en |
| Βιβλιογραφική Αναφορά | 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 | en |
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