Stochastic approximation based PCA and its application to identification of EIV systems
Wenxiao Zhao, Han-Fu Chen · 2012
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 + εkwith 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 (EIV) systems, and the estimates are also strongly consistent. The performance of SAPCA algorithm is testified by a simulation example.