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Rank-R FNN: a tensor-based learning model for high-order data classification

Makantasis Konstantinos, Georgogiannis Alexandros, Voulodimos, Athanasios, Georgoulas Ioannis, Doulamis, Anastasios, Doulamis, Nikolaos

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URIhttp://purl.tuc.gr/dl/dias/61319727-2026-4DE8-B397-F0AC2181F782-
Identifierhttps://doi.org/10.1109/ACCESS.2021.3072973-
Identifierhttps://ieeexplore.ieee.org/document/9402813-
Languageen-
Extent12 pagesen
TitleRank-R FNN: a tensor-based learning model for high-order data classificationen
CreatorMakantasis Konstantinosen
CreatorΜακαντασης Κωνσταντινοςel
CreatorGeorgogiannis Alexandrosen
CreatorΓεωργογιαννης Αλεξανδροςel
CreatorVoulodimos, Athanasiosen
CreatorGeorgoulas Ioannisen
CreatorDoulamis, Anastasiosen
CreatorDoulamis, Nikolaosen
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryAn increasing number of emerging applications in data science and engineering are based on multidimensional and structurally rich data. The irregularities, however, of high-dimensional data often compromise the effectiveness of standard machine learning algorithms. We hereby propose the Rank- R Feedforward Neural Network (FNN), a tensor-based nonlinear learning model that imposes Canonical/Polyadic decomposition on its parameters, thereby offering two core advantages compared to typical machine learning methods. First, it handles inputs as multilinear arrays, bypassing the need for vectorization, and can thus fully exploit the structural information along every data dimension. Moreover, the number of the model's trainable parameters is substantially reduced, making it very efficient for small sample setting problems. We establish the universal approximation and learnability properties of Rank- R FNN, and we validate its performance on real-world hyperspectral datasets. Experimental evaluations show that Rank- R FNN is a computationally inexpensive alternative of ordinary FNN that achieves state-of-the-art performance on higher-order tensor data.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-03-02-
Date of Publication2021-
SubjectHigh-order data processingen
SubjectHyperspectral data classifcationen
SubjectRank-R FNNen
SubjectTensor-based neural networksen
Bibliographic CitationK. Makantasis, A. Georgogiannis, A. Voulodimos, I. Georgoulas, A. Doulamis and N. Doulamis, "Rank-R FNN: a tensor-based learning model for high-order data classification," IEEE Access, vol. 9, pp. 58609-58620, Apr. 2021, doi: 10.1109/ACCESS.2021.3072973.en

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