title: Novel Online Dimensionality Reduction Method with Improved Topology Representing and Radial Basis Function Networks.
abstract: This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method.
authors.affiliation: Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China.
authors.forename: Shengqiao
authors.initials: S
authors.lastname: Ni
authors.affiliation: Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China.
authors.forename: Jiancheng
authors.initials: J
authors.lastname: Lv
authors.affiliation: Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China.
authors.forename: Zhehao
authors.initials: Z
authors.lastname: Cheng
authors.affiliation: Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China.
authors.forename: Mao
authors.initials: M
authors.lastname: Li
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D058011:Multifactor Dimensionality Reduction; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D015203:Reproducibility of Results