A regularization framework for robust dimensionality reduction with applications to image reconstruction and feature extraction

DOI10.1016/j.patcog.2009.10.012zbMath1192.68586OpenAlexW2020302785MaRDI QIDQ962667

Youfu Li, Zhizheng Liang

Publication date: 7 April 2010

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2009.10.012

zbMATH Keywords

robustfeature extractionnonlinear eigenvalue problemimage reconstructionregularization frameworkSCF iteration

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Uses Software

• UCI-ml
• ElemStatLearn

Cites Work

• A study on three linear discriminant analysis based methods in small sample size problem
• Denoising of Frame Coefficients Using $\ell^1$ Data-Fidelity Term and Edge-Preserving Regularization
• A Trust Region Direct Constrained Minimization Algorithm for the Kohn–Sham Equation
• On the Convergence of the Self-Consistent Field Iteration for a Class of Nonlinear Eigenvalue Problems
• Minimizers of Cost-Functions Involving Nonsmooth Data-Fidelity Terms. Application to the Processing of Outliers
• Efficient Reconstruction of Piecewise Constant Images Using Nonsmooth Nonconvex Minimization
• Analysis of Half-Quadratic Minimization Methods for Signal and Image Recovery
• The elements of statistical learning. Data mining, inference, and prediction
• A direct LDA algorithm for high-dimensional data -- with application to face recognition
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