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Convergence of algorithms used for principal component analysis

Abstract

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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Correspondence to Junhua Zhang.

Additional information

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). https://doi.org/10.1007/BF02916844

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Keywords

  • principal component analysis
  • stochastic approximation
  • algorithms
  • convergence