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The stochastic approximation based principal component analysis (SAPCA) algorithm is introduced to recursively estimate the eigenvectors and the corresponding eigenvalues of a symmetric matrix A based on observations Ak = A + εk with εk → 0 as k → ∞. The estimates are strongly consistent. The SAPCA algorithm is then applied to identifying the matrix coefficients of the multivariate errors-in-variables...
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