MLSLR: multilabel learning via sparse logistic regression

DOI10.1016/j.ins.2014.05.013zbMath1355.68231OpenAlexW2068512396MaRDI QIDQ507663

Hua-Wen Liu, Xindong Wu, Shichao Zhang

Publication date: 7 February 2017

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2014.05.013

zbMATH Keywords

logistic regressionvariable selectionelastic netsparse learningmultilabel data

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Generalized linear models (logistic models) (62J12) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

Using tensor products to detect unconditional label dependence in multilabel classifications ⋮ Robust non-negative sparse graph for semi-supervised multi-label learning with missing labels ⋮ Sparse regression with output correlation for cardiac ejection fraction estimation

Uses Software

• MULAN
• glmnet

Cites Work

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• An ensemble method for high-dimensional multilabel data
• Least angle regression. (With discussion)
• Coordinate descent algorithms for lasso penalized regression
• Feature subset selection using separability index matrix
• Fast dimension reduction for document classification based on imprecise spectrum analysis
• An Introduction to Numerical Analysis
• Regularization and Variable Selection Via the Elastic Net
• Multilabel Classification with Principal Label Space Transformation
• Sparse Logistic Regression with Lp Penalty for Biomarker Identification

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