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

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• 作者：Xiaowei Feng ; Xiangyu Kong ; Hongguang Ma ; Haomiao Liu
• 关键词：PCA ; MCA ; Speed ; stability problem ; Coupled algorithm ; Unified algorithm ; Eigen ; pair
• 刊名：Neural Processing Letters
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：45
• 期：1
• 页码：197-222
• 全文大小：
• 刊物类别：Physics and Astronomy
• 刊物主题：Artificial Intelligence (incl. Robotics); Complex Systems; Computational Intelligence;
• 出版者：Springer US
• ISSN：1573-773X
• 卷排序：45

文摘

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.