Nonlinear feature extraction based on centroids and kernel functions

DOI10.1016/J.PATCOG.2003.07.011zbMath1068.68136OpenAlexW2109586090MaRDI QIDQ1886659

Haesun Park, Cheong Hee Park

Publication date: 18 November 2004

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

Full work available at URL: http://hdl.handle.net/11299/215543

zbMATH Keywords

Linear discriminant analysisSupport vector machinesKernel functionsPattern classificationDimension reductionCluster structureKernel orthogonal centroid methodNonlinear feature extraction

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (1)

A comparative analysis of kernel subspace target detectors for hyperspectral imagery

Uses Software

• SVMlight

Cites Work

• Lower dimensional representation of text data based on centroids and least squares
• Principal component analysis.
• Support-vector networks
• Theoretical foundations of the potential function method in pattern recognition learning
• Flexible Discriminant Analysis by Optimal Scoring
• Structure Preserving Dimension Reduction for Clustered Text Data Based on the Generalized Singular Value Decomposition
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