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

Content Provider	Springer Nature Link
Author	Feng, Xiaowei Kong, Xiangyu Ma, Hongguang Liu, Haomiao
Copyright Year	2016
Abstract	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.
Starting Page	197
Ending Page	222
Page Count	26
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	45
Issue Number	1
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2016-04-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	PCA MCA Speed-stability problem Coupled algorithm Unified algorithm Eigen-pair Artificial Intelligence (incl. Robotics) Complex Systems Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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