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 -