Learning linear PCA with convex semi-definite programming

DOI10.1016/j.patcog.2007.01.022zbMath1132.68598OpenAlexW2011488612MaRDI QIDQ2373456

Jue Wang, Qing Tao, Gao-wei Wu

Publication date: 11 July 2007

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2007.01.022

zbMATH Keywords

robustnessprincipal component analysissemi-definite programmingsupport vector machinesstatistical learning theorymarginmaximal margin algorithm

Mathematics Subject Classification ID

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

Cites Work

• A new maximum margin algorithm for one-class problems and its boosting implementation
• Boosting the margin: a new explanation for the effectiveness of voting methods
• Principal component analysis.
• A theory of the learnable
• Large-Scale Optimization of Eigenvalues
• 10.1162/15324430152748227
• Chaos control using least-squares support vector machines
• Principal component analysis based on robust estimators of the covariance or correlation matrix: influence functions and efficiencies
• Principal Curves
• Semidefinite Programming
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item