Robust dimension reduction

Cites Work

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High breakdown estimators for principal components: the projection-pursuit approach revis\-ited
Asymptotic behaviour of S-estimates of multivariate location parameters and dispersion matrices
Robust m-estimators of multivariate location and scatter
Robust principal component analysis for functional data. (With comments)
Robust kernel principal component analysis and classification
Detecting influential observations in kernel PCA
Diffusion maps
Robust locally linear embedding
Exact matrix completion via convex optimization
Robust principal component analysis?
Rank-Sparsity Incoherence for Matrix Decomposition
Reducing the Dimensionality of Data with Neural Networks
Least Median of Squares Regression
Generalized KPCA by adaptive rules in feature space
Robust Kernel Principal Component Analysis
Projection-Pursuit Approach to Robust Dispersion Matrices and Principal Components: Primary Theory and Monte Carlo
Robust Estimation of Dispersion Matrices and Principal Components
Robust Procedures in Multivariate Analysis I: Robust Covariance Estimation
Principal component analysis based on robust estimators of the covariance or correlation matrix: influence functions and efficiencies
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
Principal Curves
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
Local Multidimensional Scaling for Nonlinear Dimension Reduction, Graph Drawing, and Proximity Analysis
Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data

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