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

Kong, Xiangyu; An, Qiusheng; Ma, Hongguang; Han, Chongzhao; Zhang, Qi

doi:10.1016/j.neunet.2012.08.016

Cited by 12 publications

(1 citation statement)

References 30 publications

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“…algorithm uses different signs on only some terms in its rules of operation when implementing PCA or MCA, the hardware cost for algorithms can be reduced; and (3) the study of the dual-purpose algorithm can interpret the intrinsic link between PCA and MCA algorithms and thus is of theoretical importance. Minimal attention has been paid to dual purpose algorithms, and few researchers have examined the convergence speed of existing algorithms [11]- [15]. Therefore, developing a fast dual purpose algorithm is the aim of this paper.…”

Section: Introductionmentioning

confidence: 99%

Novel Dual-Purpose Algorithm for Principal and Minor Component Analysis

2020

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A dual-purpose algorithm is capable of estimating the principal component and minor component from input signals by simply switching the sign of some terms in the same learning rule. Compared with single-purpose algorithms, a dual-purpose algorithm has many advantages. In this paper, a novel dual-purpose algorithm is proposed based on the study of some existing algorithms. The dynamic behavior of this dual-purpose algorithm is investigated by the deterministic discrete time method. Some constraint conditions, which provide a way to choose the initial weight vector and learning factor, are also derived to guarantee its convergence. Numerical simulation results not only demonstrate the fast convergence of the proposed algorithm but also demonstrate the correctness of the convergence conditions. INDEX TERMS Dual-purpose algorithm, principal component analysis, minor component analysis, dynamic behavior analysis.

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Section: Introductionmentioning

confidence: 99%