Block coordinate descent algorithms for large-scale sparse multiclass classification

DOI10.1007/s10994-013-5367-2zbMath1293.68216OpenAlexW2072925128MaRDI QIDQ399900

Kazuhiro Seki, Mathieu Blondel, Kuniaki Uehara

Publication date: 20 August 2014

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

Full work available at URL: https://doi.org/10.1007/s10994-013-5367-2

zbMATH Keywords

block coordinate descentgroup sparsitymulticlass classification

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Kernel multilogit algorithm for multiclass classification, Semi-supervised AUC optimization based on positive-unlabeled learning, Separable approximations and decomposition methods for the augmented Lagrangian, Group variable selection via \(\ell_{p,0}\) regularization and application to optimal scoring, A random block-coordinate Douglas-Rachford splitting method with low computational complexity for binary logistic regression, Robust supervised learning with coordinate gradient descent, Subspace quadratic regularization method for group sparse multinomial logistic regression, Incremental Regularized Least Squares for Dimensionality Reduction of Large-Scale Data, Taylor quasi-likelihood for limited generalized linear models

Uses Software

• glmnet
• Pegasos
• FISTA
• SparseLOGREG

Cites Work

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