Learning from normalized local and global discriminative information for semi-supervised regression and dimensionality reduction

DOI10.1016/J.INS.2015.06.021zbMath1390.68562OpenAlexW745184511MaRDI QIDQ1750062

Tommy W. S. Chow, Bing Li, Mingbo Zhao, Zhou Wu, Zhao Zhang

Publication date: 17 May 2018

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

Full work available at URL: https://doi.org/10.1016/j.ins.2015.06.021

zbMATH Keywords

dimensionality reductionsemi-supervised learninglocal and global discriminative information

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (2)

Discriminative sparse least square regression for semi-supervised learning ⋮ Supervised learning of explicit maps with ability to correct distortions in the target output for manifold learning

Uses Software

• KPCA plus LDA
• COIL-100

Cites Work

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• Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction
• Marginal semi-supervised sub-manifold projections with informative constraints for dimensionality reduction and recognition
• A general learning framework using local and global regularization
• A general soft label based linear discriminant analysis for semi-supervised dimensionality reduction
• Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
• Image Clustering Using Local Discriminant Models and Global Integration
• TurboPixel Segmentation Using Eigen-Images

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