Robust L1 Principal Component Analysis and Its Bayesian Variational Inference

DOI10.1162/neco.2007.11-06-397zbMath1132.62048OpenAlexW2151673022WikidataQ51899599 ScholiaQ51899599MaRDI QIDQ5446249

Junbin Gao

Publication date: 6 March 2008

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco.2007.11-06-397

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Bayesian inference (62F15) Image analysis in multivariate analysis (62H35) Statistical distribution theory (62E99)

Related Items (14)

Generalized System Identification with Stable Spline Kernels ⋮ A pure \(L_1\)-norm principal component analysis ⋮ Expectation propagation for nonlinear inverse problems -- with an application to electrical impedance tomography ⋮ Variational Bayesian sparse additive matrix factorization ⋮ Generalized Kalman smoothing: modeling and algorithms ⋮ Principal component analysis based on nuclear norm minimization ⋮ Distance metric learning by minimal distance maximization ⋮ Bayesian robust principal component analysis with adaptive singular value penalty ⋮ Efficient and robust TWSVM classification via a minimum L1-norm distance metric criterion ⋮ A variational Bayesian method to inverse problems with impulsive noise ⋮ Maxi-Min discriminant analysis via online learning ⋮ Sparse kernel learning with LASSO and Bayesian inference algorithm ⋮ Bayesian Robust PCA for Incomplete Data ⋮ Joint sparse principal component analysis

Cites Work

• A robust principal component analysis
• A framework for robust subspace learning
• Probabilistic Principal Component Analysis

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