Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
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 PCA for novelty detection
Pattern Recognition
Authenticating corrupted photo images based on noise parameter estimation
Pattern Recognition
KPCA denoising and the pre-image problem revisited
Digital Signal Processing
Fuzzy peer groups for reducing mixed Gaussian-impulse noise from color images
IEEE Transactions on Image Processing
Fuzzy SVM for noisy data: a robust membership calculation method
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Robust kernel discriminant analysis using fuzzy memberships
Pattern Recognition
Quasi-objective nonlinear principal component analysis
Neural Networks
Authenticating corrupted face image based on noise model
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A distribution-free method for process monitoring
Expert Systems with Applications: An International Journal
Regularized Pre-image Estimation for Kernel PCA De-noising
Journal of Signal Processing Systems
Automatic denoising of 2d color face images using recursive PCA reconstruction
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Authenticating corrupted facial images on stand-alone DSP system
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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Recently, kernel Principal Component Analysis is becoming a popular technique for feature extraction. It enables us to extract nonlinear features and therefore performs as a powerful preprocessingstep for classification. There is one drawback, however, that extracted feature components are sensitive to outliers contained in data. This is a characteristic common to all PCA-based techniques. In this paper, we propose a method which is able to remove outliers in data vectors and replace them with the estimated values via kernel PCA. By repeatingthis process several times, we can get the feature components less affected with outliers. We apply this method to a set of face image data and confirm its validity for a recognition task.