TY - GEN

T1 - Improved nonlinear PCA based on RBF networks and principal curves

AU - Liu, Xueqin

AU - Li, Kang

AU - McAfee, Marion

AU - Deng, Jing

PY - 2010

Y1 - 2010

N2 - Nonlinear PCA based on neural networks (NN) have been widely used in different applications in the past decade. There is a difficulty with the determination of the optimal topology for the networks that are used. Principal curves were introduced to nonlinear PCA to separate the original complex five-layer NN into two three-layer RBF networks and eased the above problem. Using the advantage of Fast Recursive Algorithm, where the number of neurons, the location of centers, and the weights between the hidden layer and the output layer can be identified simultaneously for the RBF networks, the topology problem for the nonlinear PCA based on NN can thus be solved. The simulation result shows that the method is excellent for solving nonlinear principal component problems.

AB - Nonlinear PCA based on neural networks (NN) have been widely used in different applications in the past decade. There is a difficulty with the determination of the optimal topology for the networks that are used. Principal curves were introduced to nonlinear PCA to separate the original complex five-layer NN into two three-layer RBF networks and eased the above problem. Using the advantage of Fast Recursive Algorithm, where the number of neurons, the location of centers, and the weights between the hidden layer and the output layer can be identified simultaneously for the RBF networks, the topology problem for the nonlinear PCA based on NN can thus be solved. The simulation result shows that the method is excellent for solving nonlinear principal component problems.

UR - http://www.scopus.com/inward/record.url?scp=78649586262&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-15621-2_2

DO - 10.1007/978-3-642-15621-2_2

M3 - Conference contribution

AN - SCOPUS:78649586262

SN - 3642156207

SN - 9783642156205

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 7

EP - 15

BT - Life System Modeling and Intelligent Computing - International Conference on LSMS 2010 and ICSEE 2010, Proceedings

T2 - 2010 International Conference on Life System Modeling and Simulation, LSMS 2010 and the 2010 International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2010

Y2 - 17 September 2010 through 20 September 2010

ER -