A column-wise update algorithm for nonnegative matrix factorization in Bregman divergence with an orthogonal constraint

DOI10.1007/S10994-016-5553-0zbMath1383.62178OpenAlexW2341301259MaRDI QIDQ298290

Mineichi Kudo, Keigo Kimura, Yuzuru Tanaka

Publication date: 20 June 2016

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-016-5553-0

zbMATH Keywords

orthogonal factorizationBregman divergencecolumn-wise updateorthogonal nonnegative matrix factorization

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Convex programming (90C25)

Related Items (3)

Randomized algorithms for orthogonal nonnegative matrix factorization ⋮ Unnamed Item ⋮ Leveraging maximum entropy and correlation on latent factors for learning representations

Cites Work

• Unnamed Item
• Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework
• On the equivalence between non-negative matrix factorization and probabilistic latent semantic indexing
• A convergent algorithm for orthogonal nonnegative matrix factorization
• Fast Nonnegative Matrix Factorization: An Active-Set-Like Method and Comparisons
• On the Optimality of Conditional Expectation as a Bregman Predictor
• Projected Gradient Methods for Nonnegative Matrix Factorization

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