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• W2562604834 abstract "To deal with large multimodal datasets, coupled canonical polyadic decompositions are used as an approximation model. In this paper, a joint compression scheme is introduced to reduce the dimensions of the dataset. Joint compression allows to solve the approximation problem in a compressed domain using standard coupled decomposition algorithms. Computational complexity required to obtain the coupled decomposition is therefore reduced. Also, we propose to approximate the update of the coupled factor by a simple weighted average of the independent updates of the coupled factors. The proposed approach and its simplified version are tested with synthetic data and we show that both do not incur substantial loss in approximation performance." @default.
• W2562604834 created "2017-01-06" @default.
• W2562604834 creator A5033972557 @default.
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• W2562604834 date "2016-08-01" @default.
• W2562604834 modified "2023-09-24" @default.
• W2562604834 title "Joint tensor compression for coupled canonical polyadic decompositions" @default.
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• W2562604834 doi "https://doi.org/10.1109/eusipco.2016.7760656" @default.
• W2562604834 hasPublicationYear "2016" @default.
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