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Nonnegative PARAFAC2: a flexible coupling approach

Cohen, Jeremy E., Bro, Rasmus

arXiv.org Machine LearningFeb-14-2018

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 PARAF AC2 model. However, so far imposing constraints on the mode with variability has not been possible. In the following manuscript, a relaxation of the PARAF AC2 model is introduced, that allows for imposing nonnegativity constraints on the varying mode. An algorithm to compute the proposed flexible PARAF AC2 model is derived, and its performance is studied on both synthetic and chemometrics data.

ac2 model, paraf ac2, paraf ac2 model, (14 more...)

arXiv.org Machine Learning

1802.05035

Country:

North America > United States > California > Santa Clara County > Santa Clara (0.04)
Europe > Denmark > Capital Region > Copenhagen (0.04)

Genre: Research Report (0.64)

Technology:

Information Technology > Artificial Intelligence > Machine Learning (0.89)
Information Technology > Artificial Intelligence > Representation & Reasoning (0.70)

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