Two heuristic algorithms (fixed-point from penalized KKT and staged ADAM) are proposed for symmetric multi-type orthogonal NMF tri-factorization and evaluated on synthetic noisy data and citation networks for recovery and downstream tasks.
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Neural Computation 19(10), 2756–2779 (2007)
2 Pith papers cite this work, alongside 1,776 external citations. Polarity classification is still indexing.
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GNRBMF extends NRBMF by adding graph Laplacian regularization on the coefficient matrix to encourage similar representations for nearby samples while retaining non-negativity in the reduced biquaternion domain.
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On solving symmetric multi-type orthogonal non-negative matrix tri-factorization problem
Two heuristic algorithms (fixed-point from penalized KKT and staged ADAM) are proposed for symmetric multi-type orthogonal NMF tri-factorization and evaluated on synthetic noisy data and citation networks for recovery and downstream tasks.
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Graph Regularized Non-negative Reduced Biquaternion Matrix Factorization for Color Image Recognition
GNRBMF extends NRBMF by adding graph Laplacian regularization on the coefficient matrix to encourage similar representations for nearby samples while retaining non-negativity in the reduced biquaternion domain.