Penalized preimage learning in kernel principal component analysis
IEEE Transactions on Neural Networks
LPP solution schemes for use with face recognition
Pattern Recognition
Regularized Pre-image Estimation for Kernel PCA De-noising
Journal of Signal Processing Systems
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In this paper, we address the pre-image problem in kernel principal component analysis (KPCA). The preimage problem finds a pattern as the pre-image of a feature vector defined in the nonlinear principal component space produced by KPCA. Since the preimage typically seldom exists in general, an approximate solution is appreciated. By posing a novel perspective, we find the pre-image with regularized locality preserving learning. Our approach achieves a unique solution, avoiding iteration and numerical instability. Significant superiority of the proposed novel algorithm is demonstrated by driving two applications, namely face denoising and occluded face reconstruction, as comparing with some existing wellknown methods on pre-image learning.