Characterization and detection of noise in clustering
Pattern Recognition Letters
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Random Data: Analysis and Measurement Procedures
Random Data: Analysis and Measurement Procedures
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
KPCA for semantic object extraction in images
Pattern Recognition
Kernel principal component analysis for content based image retrieval
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Outlier resistant PCA ensembles
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Fuzzy auto-associative neural networks for principal component extraction of noisy data
IEEE Transactions on Neural Networks
Robust principal component analysis by self-organizing rules based on statistical physics approach
IEEE Transactions on Neural Networks
2009 Special Issue: RKF-PCA: Robust kernel fuzzy PCA
Neural Networks
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Principal component analysis (PCA) is widely used for dimensionality reduction in pattern recognition. Although PCA has been applied in many areas successfully, it suffers from sensitivity to noise and is limited to linear principal components. The noise sensitivity problem comes from the least-squares measure used in PCA and the limitation to linear components originates from the fact that PCA uses an affine transform defined by eigenvectors of the covariance matrix and the mean of the data. In this paper, a robust kernel PCA method that extends Schölkopf et al.'s kernel PCA and uses fuzzy memberships is introduced to tackle the two problems simultaneously. We first propose an iterative method to find a robust covariance matrix called Robust Fuzzy PCA (RF-PCA). The RF-PCA is introduced to reduce the sensitivity to noise with the help of robust estimation technique. The RF-PCA method is then extended to a non-linear one, Robust Kernel Fuzzy PCA (RKF-PCA), using kernels. Experimental results suggest that the proposed algorithm works well on artificial and real world data sets.