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Multi-modal multi-view emotion detection using non-negative matrix factorisation

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dc.contributor.author GROZAVU, Nistor
dc.contributor.author KHALAFAOUI, Yasser
dc.contributor.author ROGOVSCHI, Nicoleta
dc.date.accessioned 2022-12-29T11:53:22Z
dc.date.available 2022-12-29T11:53:22Z
dc.date.issued 2022
dc.identifier.citation GROZAVU, Nistor, KHALAFAOUI, Yasser, ROGOVSCHI, Nicoleta. Multi-modal multi-view emotion detection using non-negative matrix factorisation. In: Electronics, Communications and Computing (IC ECCO-2022): 12th intern. conf., 20-21 Oct. 2022, Chişinău, Republica Moldova: conf. proc., Chişinău, 2022, pp. 21. en_US
dc.identifier.uri http://repository.utm.md/handle/5014/21892
dc.description Only Abstract
dc.description.abstract Through this work we explore the unsupervised topological learning of multimodal data presenting a complex structure allowing to learn their representations. We are particularly interested in heterogeneous data whose representation may have been informed in different ways: expert representation which may be complex. en_US
dc.language.iso en en_US
dc.publisher Technical University of Moldova en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject multimodal data en_US
dc.subject unsupervised topological learning en_US
dc.title Multi-modal multi-view emotion detection using non-negative matrix factorisation en_US
dc.type Article en_US


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  • 2022
    Proceedings of the 12th IC|ECCO; October 20-21, 2022

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Attribution-NonCommercial-NoDerivs 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States

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