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Convergence of algorithms used for principal component analysis


The convergence of algorithms used for principal component analysis is analyzed. The algorithms are proved to converge to eigenvectors and eigenvalues of a matrixA which is the expectation of observed random samples. The conditions required here are considerably weaker than those used in previous work.

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Corresponding author

Correspondence to Junhua Zhang.

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Project supported by the National Natural Science Foundation of China.

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Zhang, J., Chen, H. Convergence of algorithms used for principal component analysis. Sci. China Ser. E-Technol. Sci. 40, 597–604 (1997).

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  • principal component analysis
  • stochastic approximation
  • algorithms
  • convergence