Abstract
Principal Component Analysis (PCA) is one of the most important feature extraction methods, and Kernel Principal Component Analysis (KPCA) is a nonlinear extension of PCA based on kernel methods. In real world, each input data may not be fully assigned to one class and it may partially belong to other classes. Based on the theory of fuzzy sets, this paper presents Fuzzy Principal Component Analysis (FPCA) and its nonlinear extension model, i.e., Kernel-based Fuzzy Principal Component Analysis (KFPCA). The experimental results indicate that the proposed algorithms have good performances.
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Wu, X., Zhou, J. Fuzzy principal component analysis and its Kernel-based model. J. Electron.(China) 24, 772–775 (2007). https://doi.org/10.1007/s11767-006-0039-z
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DOI: https://doi.org/10.1007/s11767-006-0039-z