mlsanityData to 7 Oct 2026

Nonnegative PARAFAC2: a flexible coupling approach

arXiv 1802.05035 ↗First posted 14 Feb 2018stat.MLcited by 11

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Abstract (arXiv)

Modeling variability in tensor decomposition methods is one of the challenges of source separation. One possible solution to account for variations from one data set to another, jointly analysed, is to resort to the PARAFAC2 model. However, so far imposing constraints on the mode with variability has not been possible. In the following manuscript, a relaxation of the PARAFAC2 model is introduced, that allows for imposing nonnegativity constraints on the varying mode. An algorithm to compute the proposed flexible PARAFAC2 model is derived, and its performance is studied on both synthetic and chemometrics data.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Feb 2018.

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Citing papers per year: 2021: 3; 2022: 4; 2023: 1; 2024: 3.

11 papers.

  1. 2021An AO-ADMM approach to constraining PARAFAC2 on all modescited by 12
  2. 2022Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositionscited by 4
  3. 2022PARAFAC2-based Coupled Matrix and Tensor Factorizationscited by 3
  4. 2024Randomized Algorithms for Symmetric Nonnegative Matrix Factorizationcited by 3
  5. 2021PARAFAC2 AO-ADMM: Constraints in all modescited by 2
  6. 2022PARAFAC2×N: Coupled Decomposition of Multi-modal Data with Drift in N Modescited by 2
  7. 2022Untargeted Region of Interest Selection for GC-MS Data using a Pseudo F-Ratio Moving Window (psiFRMV)cited by 1
  8. 2023SWoTTeD: An Extension of Tensor Decomposition to Temporal Phenotypingcited by 1
  9. 2024PARAFAC2-based Coupled Matrix and Tensor Factorizations with Constraintscited by 1
  10. 2024tPARAFAC2: Tracking evolving patterns in (incomplete) temporal datacited by 1
  11. 2021Multi-Channel Automatic Music Transcription Using Tensor Algebracited by 0