Semi-supervised classification based on random subspace dimensionality reduction

DOI10.1016/j.patcog.2011.08.024zbMath1227.68096OpenAlexW2132392542MaRDI QIDQ650963

Guoji Zhang, Carlotta Domeniconi, Zhiwen Yu, Guoxian Yu, Jane You

Publication date: 7 December 2011

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2011.08.024

zbMATH Keywords

dimensionality reductionsemi-supervised classificationgraph constructionrandom subspacesensembles of classifiers

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (8)

Inductive and flexible feature extraction for semi-supervised pattern categorization ⋮ A second order cone programming approach for semi-supervised learning ⋮ Approximate polytope ensemble for one-class classification ⋮ A general framework for dimensionality reduction of K-means clustering ⋮ Pairwise constraints based multiview features fusion for scene classification ⋮ Semi-supervised classification via simultaneous label and discriminant embedding estimation ⋮ SSC-EKE: semi-supervised classification with extensive knowledge exploitation ⋮ Multiple graph regularized graph transduction via greedy gradient Max-Cut

Uses Software

• AR face
• Learn++.MF

Cites Work

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• Bagging predictors
• Graph-based classification of multiple observation sets
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• A linear discriminant analysis framework based on random subspace for face recognition
• Learn\(^{++}\).MF: A random subspace approach for the missing feature problem
• Principal component analysis.
• Boosting in Random Subspace for Face Recognition
• A direct LDA algorithm for high-dimensional data -- with application to face recognition

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