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Coupled Cross-correlation Neural Network Algorithm for Principal Singular Triplet Extraction of a Cross-covariance Matrix
被引量:
2
1
作者
Xiaowei Feng
Xiangyu Kong
Hongguang Ma
《IEEE/CAA Journal of Automatica Sinica》
SCIE
EI
2016年第2期149-156,共8页
This paper proposes a novel coupled neural network learning algorithm to extract the principal singular triplet (PST) of a cross-correlation matrix between two high-dimensional data streams. We firstly introduce a nov...
This paper proposes a novel coupled neural network learning algorithm to extract the principal singular triplet (PST) of a cross-correlation matrix between two high-dimensional data streams. We firstly introduce a novel information criterion (NIC), in which the stationary points are singular triplet of the crosscorrelation matrix. Then, based on Newton's method, we obtain a coupled system of ordinary differential equations (ODEs) from the NIC. The ODEs have the same equilibria as the gradient of NIC, however, only the first PST of the system is stable (which is also the desired solution), and all others are (unstable) saddle points. Based on the system, we finally obtain a fast and stable algorithm for PST extraction. The proposed algorithm can solve the speed-stability problem that plagues most noncoupled learning rules. Moreover, the proposed algorithm can also be used to extract multiple PSTs effectively by using sequential method. © 2014 Chinese Association of Automation.
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关键词
Clustering
algorithms
Covariance
matrix
Data
mining
Differential
equations
EXTRACTION
Learning
algorithms
Negative
impedance
converters
Newton
Raphson
method
Ordinary
differential
equations
singular
value
decomposition
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职称材料
题名
Coupled Cross-correlation Neural Network Algorithm for Principal Singular Triplet Extraction of a Cross-covariance Matrix
被引量:
2
1
作者
Xiaowei Feng
Xiangyu Kong
Hongguang Ma
机构
Xi’an Research Institute of High Technology
Beijing Institute of Technology
出处
《IEEE/CAA Journal of Automatica Sinica》
SCIE
EI
2016年第2期149-156,共8页
基金
supported by National Natural Science Foundation of China(61174207,61374120,61074072,11405267)
文摘
This paper proposes a novel coupled neural network learning algorithm to extract the principal singular triplet (PST) of a cross-correlation matrix between two high-dimensional data streams. We firstly introduce a novel information criterion (NIC), in which the stationary points are singular triplet of the crosscorrelation matrix. Then, based on Newton's method, we obtain a coupled system of ordinary differential equations (ODEs) from the NIC. The ODEs have the same equilibria as the gradient of NIC, however, only the first PST of the system is stable (which is also the desired solution), and all others are (unstable) saddle points. Based on the system, we finally obtain a fast and stable algorithm for PST extraction. The proposed algorithm can solve the speed-stability problem that plagues most noncoupled learning rules. Moreover, the proposed algorithm can also be used to extract multiple PSTs effectively by using sequential method. © 2014 Chinese Association of Automation.
关键词
Clustering
algorithms
Covariance
matrix
Data
mining
Differential
equations
EXTRACTION
Learning
algorithms
Negative
impedance
converters
Newton
Raphson
method
Ordinary
differential
equations
singular
value
decomposition
Keywords
singular
value decomposition(SVD)
coupled algorithm
cross-correlation neural network(CNN)
speed-stability problem
principal
singular
subspace(PSS)
principal
singular
triplet
(
pst
)
分类号
TP183 [自动化与计算机技术—控制理论与控制工程]
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Coupled Cross-correlation Neural Network Algorithm for Principal Singular Triplet Extraction of a Cross-covariance Matrix
Xiaowei Feng
Xiangyu Kong
Hongguang Ma
《IEEE/CAA Journal of Automatica Sinica》
SCIE
EI
2016
2
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