Abstract
A methodology has been developed for performing stepwise orthogonal decomposition of a data set using a derivative of the backpropagation neural network algorithm. The network is divided into two parts: a linear orthogonal feature extraction portion, and a nonlinear mapping portion consisting of one or more layers. Error feedback from the nonlinear mapping portion is used to direct the feature extraction process. In addition, the use of multivariate Gaussian kernel function nodes has been generalized to allow inclusion as any backpropagation network node. The derivation of these algorithms is described, and some of their properties are illustrated with controlled artificial data distributions.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
