Convergence Analysis of Möller Algorithm for Estimating Minor Component

Gao, Yingbin; Kong, Xiangyu; Hu, Changhua; Zhang, Huihui; Hou, Lijun

doi:10.1007/s11063-014-9360-y

Cited by 4 publications

(1 citation statement)

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“…When the DDT method is employed, the dynamic property of a weight vector can be obtained by projecting the weight vector onto all of the eigenvectors of the autocorrelation matrix and analyzing the changing laws of these projections. Compared with other methods, the DDT analysis method can retain the discrete nature and the dynamic properties of the algorithm [18] and thus has been favored by many investigators and extensively applied in many PCA or MCA algorithms [18]- [20]. Compared with PCA or MCA algorithms, the structure of dualpurpose algorithms is more complicated, and the difficulty in analysis increases correspondingly.…”

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%