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Curve Registered Coupled Low Rank Factorization

Abstract : We propose an extension of the canonical polyadic (CP) tensor model where one of the latent factors is allowed to vary through data slices in a constrained way. The components of the latent factors, which we want to retrieve from data, can vary from one slice to another up to a diffeomorphism. We suppose that the diffeomorphisms are also unknown, thus merging curve registration and tensor decomposition in one model, which we call registered CP. We present an algorithm to retrieve both the latent factors and the diffeomorphism, which is assumed to be in a parametrized form. At the end of the paper, we show simulation results comparing registered CP with other models from the literature.
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Contributor : Rodrigo Cabral Farias Connect in order to contact the contributor
Submitted on : Friday, February 16, 2018 - 9:03:47 AM
Last modification on : Saturday, June 25, 2022 - 11:29:02 PM

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  • HAL Id : hal-01710498, version 1
  • ARXIV : 1802.03203



Jérémy E Cohen, Rodrigo Cabral Farias, Bertrand Rivet. Curve Registered Coupled Low Rank Factorization. LVA/ICA 2018 - 14th International Conference on Latent Variable Analysis and Signal Separation, Jul 2018, Guildford, United Kingdom. ⟨hal-01710498⟩



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