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Joint Tensor Compression for Coupled Canonical Polyadic Decompositions

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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.
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Dates and versions

hal-01326132 , version 1 (03-06-2016)

Identifiers

  • HAL Id : hal-01326132 , version 1

Cite

Jérémy E Cohen, Rodrigo Cabral Farias, Pierre Comon. Joint Tensor Compression for Coupled Canonical Polyadic Decompositions. EUSIPCO 2016 - 24th European Signal Processing Conference, Aug 2016, Budapest, Hungary. ⟨hal-01326132⟩
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