Weighted co-association rate-based Laplacian regularized label description for semi-supervised regression

DOI10.1016/j.ins.2020.09.015zbMath1479.62048OpenAlexW3087009257MaRDI QIDQ2054054

Youngdoo Son, Jaehong Yu

Publication date: 30 November 2021

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

Full work available at URL: https://doi.org/10.1016/j.ins.2020.09.015

zbMATH Keywords

smoothness regularizationlabel-descriptive functionsemi-supervised regressionweighted co-association rate

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) General nonlinear regression (62J02)

Uses Software

• AS 136
• Spearmint

Cites Work

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• Algorithm AS 136: A K-Means Clustering Algorithm
• An ensemble feature ranking algorithm for clustering analysis
• Robust non-negative sparse graph for semi-supervised multi-label learning with missing labels
• Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm
• A clustering ensemble: two-level-refined co-association matrix with path-based transformation
• Consensus rate-based label propagation for semi-supervised classification
• Semi-supervised sparse feature selection via graph Laplacian based scatter matrix for regression problems
• Error bounds of multi-graph regularized semi-supervised classification

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