Sparse and low-rank matrix regularization for learning time-varying Markov networks

DOI10.1007/s10994-016-5568-6zbMath1454.68122OpenAlexW2465084082WikidataQ55499129 ScholiaQ55499129MaRDI QIDQ1689602

Jun-ichiro Hirayama, Shin Ishii, Aapo Hyvärinen

Publication date: 12 January 2018

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

Full work available at URL: https://doi.org/10.1007/s10994-016-5568-6

zbMATH Keywords

alternating direction method of multipliersL1-norm regularizationnuclear-norm regularizationtime-varying Markov network

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Probabilistic graphical models (62H22)

Related Items (1)

CMD: controllable matrix decomposition with global optimization for deep neural network compression

Uses Software

• PDCO
• glasso
• apcluster
• APCluster
• minFunc
• KELLER

Cites Work

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