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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Communication dans un congrès
14th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2018), Jul 2018, Univ. of Surrey, Guildford, United Kingdom. Springer, Latent Variable Analysis and Signal Separation, 10891, 2018, Theoretical Computer Science and General Issues
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https://hal.archives-ouvertes.fr/hal-01710498
Contributeur : Rodrigo Cabral Farias <>
Soumis le : vendredi 16 février 2018 - 09:03:47
Dernière modification le : mercredi 10 octobre 2018 - 10:09:40

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

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Jérémy E. Cohen, Rodrigo Cabral Farias, Bertrand Rivet. Curve Registered Coupled Low Rank Factorization. 14th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2018), Jul 2018, Univ. of Surrey, Guildford, United Kingdom. Springer, Latent Variable Analysis and Signal Separation, 10891, 2018, Theoretical Computer Science and General Issues. 〈hal-01710498〉

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