Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
High breakdown estimators for principal components: the projection-pursuit approach revisited
Journal of Multivariate Analysis
Statistical properties of kernel principal component analysis
Machine Learning
Principal component analysis for data containing outliers and missing elements
Computational Statistics & Data Analysis
Robust PCA for skewed data and its outlier map
Computational Statistics & Data Analysis
Robust probabilistic PCA with missing data and contribution analysis for outlier detection
Computational Statistics & Data Analysis
Editorial: Special issue on variable selection and robust procedures
Computational Statistics & Data Analysis
Detecting influential data points for the Hill estimator in Pareto-type distributions
Computational Statistics & Data Analysis
Robust kernel density estimation
The Journal of Machine Learning Research
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Kernel Principal Component Analysis extends linear PCA from a Euclidean space to any reproducing kernel Hilbert space. Robustness issues for Kernel PCA are studied. The sensitivity of Kernel PCA to individual observations is characterized by calculating the influence function. A robust Kernel PCA method is proposed by incorporating kernels in the Spherical PCA algorithm. Using the scores from Spherical Kernel PCA, a graphical diagnostic is proposed to detect points that are influential for ordinary Kernel PCA.