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- W2352933757 abstract "Kernel Principal Component Analysis(KPCA) is the generalized algorithm of famous Principal Component Analysis(PCA),which uses the kernel method and treats with the complex nonlinear dataset.It firstly computes the kernel matrix between mapped samples in high dimensional space,and uses eigen-decomposition technique to compute the eigen-solution for kernel matrix.The space and time complexity of the KPCA is O(m2) and O(m3),respectively.When faced with large-scale data set,the method is infeasible for the sake of the storage and computational problem.In this paper,the Power iteration is introduced to compute the highest eigen-solution.Then the Schur-Weilandt deflation is repeatedly applied to achieve other higher order eigenvectors.In the process of computation,the kernel matrix needs not to compute and store in advance.The space complexity of the proposed method is only O(m).The effectiveness of proposed method is validated from experimental results on toy and real dataset." @default.
- W2352933757 created "2016-06-24" @default.
- W2352933757 creator A5068739342 @default.
- W2352933757 date "2013-01-01" @default.
- W2352933757 modified "2023-09-24" @default.
- W2352933757 title "To Solve Kernel Principal Component Analysis Using Iterative Method" @default.
- W2352933757 hasPublicationYear "2013" @default.
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