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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/BFEDEB8E-BD37-4214-B3C4-3F9781138C19 | - |
| Identifier | https://doi.org/10.1109/ICASSP.2019.8682616 | - |
| Identifier | https://ieeexplore.ieee.org/document/8682616 | - |
| Language | en | - |
| Extent | 5 pages | en |
| Title | Common mode patterns for supervised tensor subspace learning | en |
| Creator | Makantasis Konstantinos | en |
| Creator | Μακαντασης Κωνσταντινος | el |
| Creator | Doulamis, Anastasios | en |
| Creator | Doulamis Nikolaos D. | en |
| Creator | Voulodimos, Athanasios | en |
| Publisher | Institute of Electrical and Electronics Engineers | en |
| Content Summary | In this work we propose a method for reducing the dimensionality of tensor objects in a binary classification framework. The proposed Common Mode Patterns method takes into consideration the labels' information, and ensures that tensor objects that belong to different classes do not share common features after the reduction of their dimensionality. We experimentally validate the proposed supervised subspace learning technique and compared it against Multilinear Principal Component Analysis using a publicly available hyper-spectral imaging dataset. Experimental results indicate that the proposed CMP method can efficiently reduce the dimensionality of tensor objects, while, at the same time, increasing the inter-class separability. | en |
| Type of Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Full Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2020-06-15 | - |
| Date of Publication | 2019 | - |
| Subject | Common mode patterns | en |
| Subject | Supervised tensor subspace learning | en |
| Subject | Tensor dimensionality reduction | en |
| Bibliographic Citation | K. Makantasis, A. Doulamis, N. Doulamis and A. Voulodimos, "Common mode patterns for supervised tensor subspace learning," in 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, 2019, pp. 2927-2931. doi: 10.1109/ICASSP.2019.8682616 | en |
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