Principal Component Analysis

Du, Ke-Lin; Swamy, M. N. S.

doi:10.1007/978-1-4471-7452-3_13

Cited by 2 publications

(1 citation statement)

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“…In the field of signal processing, principal component analysis (PCA) is a technique that can be performed to estimate the eigenvector that corresponds to the maximum eigenvalue of the signal autocorrelation matrix. Minor component analysis (MCA) can be used to extract the eigenvector that corresponds to the minimum eigenvalue of the signal autocorrelation matrix [1]. PCA and MCA have been applied in many areas of signal processing [2]- [4].…”

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%