Feature extraction using constrained maximum variance mapping

DOI10.1016/j.patcog.2008.05.014zbMath1167.68437OpenAlexW2016293853MaRDI QIDQ941563

B. E. Eshmatov

Publication date: 1 September 2008

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2008.05.014

zbMATH Keywords

subspacefeature extractionLDAUDPLPPmulti-manifolds learningMVP

Mathematics Subject Classification ID

Nonnumerical algorithms (68W05) Pattern recognition, speech recognition (68T10)

Related Items (2)

Adaptive tangent distance classifier on recognition of handwritten digits ⋮ A multi-manifold discriminant analysis method for image feature extraction

Uses Software

• FERET
• CMU PIE

Cites Work

• Principal component analysis.
• An efficient algorithm to solve the small sample size problem for LDA
• Local structure based supervised feature extraction
• Robust locally linear embedding
• Supervised Dimension Reduction of Intrinsically Low-Dimensional Data
• 10.1162/153244304322972667
• Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Semidefinite Programming
• Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data

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