Discriminative sparse least square regression for semi-supervised learning

DOI10.1016/j.ins.2023.03.128OpenAlexW4362608250MaRDI QIDQ6124685

No author found.

Publication date: 28 March 2024

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

Full work available at URL: https://doi.org/10.1016/j.ins.2023.03.128

zbMATH Keywords

semi-supervised learningleast square regressionmanifold regularizationinter-class sparsity

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Learning and adaptive systems in artificial intelligence (68T05) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Cites Work

• Learning from normalized local and global discriminative information for semi-supervised regression and dimensionality reduction
• Matrix exponential based discriminant locality preserving projections for feature extraction
• Inter-class sparsity based discriminative least square regression
• Flexible unsupervised feature extraction for image classification
• Discriminative Elastic-Net Regularized Linear Regression
• Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization
• Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
• Ridge Regression: Biased Estimation for Nonorthogonal Problems

This page was built for publication: Discriminative sparse least square regression for semi-supervised learning