Effective dimensionality reduction using kernel locality preserving partial least squares discriminant analysis

DOI10.1007/s40840-023-01479-1OpenAlexW4323360719MaRDI QIDQ2699576

Muhammad Aminu, Noor Atinah Ahmad

Publication date: 19 April 2023

Published in: Bulletin of the Malaysian Mathematical Sciences Society. Second Series (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s40840-023-01479-1

zbMATH Keywords

kernel methodsdimensionality reductionmanifold learningpartial least squares discriminant analysis

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Pattern recognition, speech recognition (68T10)

Uses Software

• SGL-SVM
• GLSNN

Cites Work

• Global-and-local-structure-based neural network for fault detection
• Feature selection using neighborhood entropy-based uncertainty measures for gene expression data classification
• SGL-SVM: a novel method for tumor classification via support vector machine with sparse group lasso
• Multi-class tumor classification by discriminant partial least squares using microarray gene expression data and assessment of classification models
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• A self-organizing principle for learning nonlinear manifolds
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