\(l_{0}\)-norm based structural sparse least square regression for feature selection

DOI10.1016/J.PATCOG.2015.06.003zbMath1394.68288OpenAlexW2205061450MaRDI QIDQ1669627

Zhengya Sun, Hongwei Hao, Jiuqi Han

Publication date: 3 September 2018

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2015.06.003

zbMATH Keywords

feature selectionadaptive greedy algorithmleast-square regression\(l_{0}\)-normstructural sparse learning

Mathematics Subject Classification ID

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

Related Items (2)

Convex Optimization for Group Feature Selection in Networked Data ⋮ New feature selection paradigm based on hyper-heuristic technique

Uses Software

• LIBSVM
• t-SNE
• COIL-20
• foba

Cites Work

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• Decoding by Linear Programming
• A Fast Approach for Overcomplete Sparse Decomposition Based on Smoothed $\ell ^{0}$ Norm
• Matching pursuits with time-frequency dictionaries
• Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations
• Model Selection and Estimation in Regression with Grouped Variables
• Computational Methods of Feature Selection

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