Convergence analysis of deterministic discrete time system of a unified self-stabilizing algorithm for PCA and MCA

摘要

Principal component analysis (PCA) and minor component analysis (MCA) provide powerful techniques in many information processing fields. For example, PCA is a useful tool in feature extraction, data compression, pattern recognition, time series prediction, etc. (Lv, Zhang & Tan, 2006), and MCA has been applied in frequency estimation, bearing estimation, digit beamforming, moving target indication, clutter cancellation, total least squares, computer vision, etc (Cirrincione, Cirrincione, Herault, & Huffel, 2002). Neural networks can be used to solve the task of PCA and MCA, which possess many obvious advantages, such as lower computational complexity and better suitability for high-dimensional and nonstationary data, compared with the traditional algebraic approaches.