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Unified and Coupled Self-Stabilizing Algorithms for Minor and Principal Eigen-pairs Extraction

机译：统一和耦合自稳定算法，用于次要和主要特征对的提取

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摘要

Neural network algorithms on principal component analysis (PCA) and minor component analysis (MCA) are of importance in signal processing. Unified (dual purpose) algorithm is capable of both PCA and MCA, thus it is valuable for reducing the complexity and the cost of hardware implementations. Coupled algorithm can mitigate the speed-stability problem which exists in most noncoupled algorithms. Though unified algorithm and coupled algorithm have these advantages compared with single purpose algorithm and noncoupled algorithm, respectively, there are only few of unified algorithms and coupled algorithms have been proposed. Moreover, to the best of the authors' knowledge, there is no algorithm which is both unified and coupled has been proposed. In this paper, based on a novel information criterion, we propose two self-stabilizing algorithms which are both unified and coupled. In the derivation of our algorithms, it is easier to obtain the results compared with traditional methods, because it is not needed to calculate the inverse Hessian matrix. Experiment results show that the proposed algorithms perform better than existing coupled algorithms and unified algorithms.
机译：基于主成分分析（PCA）和次要成分分析（MCA）的神经网络算法在信号处理中非常重要。统一（双重用途）算法既可以用于PCA也可以用于MCA，因此对于降低硬件实现的复杂性和成本非常有价值。耦合算法可以缓解大多数非耦合算法中存在的速度稳定性问题。尽管统一算法和耦合算法分别比单目的算法和非耦合算法具有这些优势，但提出的统一算法和耦合算法很少。而且，据作者所知，还没有提出统一和耦合的算法。本文基于一种新颖的信息准则，提出了两种统一且耦合的自稳定算法。在推导我们的算法时，与传统方法相比，更容易获得结果，因为不需要计算逆黑森州矩阵。实验结果表明，该算法的性能优于现有的耦合算法和统一算法。

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