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Novel Online Dimensionality Reduction Method with Improved Topology Representing and Radial Basis Function Networks.

Ni S, Lv J, Cheng Z, Li M - PLoS ONE (2015)

Bottom Line: 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.Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network.Experiments illustrate the effectiveness of the proposed method.

View Article: PubMed Central - PubMed

Affiliation: Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China.

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.

No MeSH data available.


Mapping quality for AFs.
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pone.0131631.g011: Mapping quality for AFs.

Mentions: ITRN-ERBF and other methods were used for the task of visual perception. The resulting two-dimensional projection of training patterns obtained by ITRN-ERBF is given in Figs 9 and 10. A comparison of the mapping quality is presented in Figs 11 and 12 as well as Table 1. Blue plusses represent the two-dimensional projections of training patterns and red circles represent testing patterns’ position. For easy inspection, only part of the training patterns’ corresponding images were plotted. The major articulation features of the AF, left-right (x-axis) and up-bottom (y-axis), are captured from the input space. For the “2” dataset, the bottom loop (x-axis) and lean (y-axis) are captured from input space.


Novel Online Dimensionality Reduction Method with Improved Topology Representing and Radial Basis Function Networks.

Ni S, Lv J, Cheng Z, Li M - PLoS ONE (2015)

Mapping quality for AFs.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4498733&req=5

pone.0131631.g011: Mapping quality for AFs.
Mentions: ITRN-ERBF and other methods were used for the task of visual perception. The resulting two-dimensional projection of training patterns obtained by ITRN-ERBF is given in Figs 9 and 10. A comparison of the mapping quality is presented in Figs 11 and 12 as well as Table 1. Blue plusses represent the two-dimensional projections of training patterns and red circles represent testing patterns’ position. For easy inspection, only part of the training patterns’ corresponding images were plotted. The major articulation features of the AF, left-right (x-axis) and up-bottom (y-axis), are captured from the input space. For the “2” dataset, the bottom loop (x-axis) and lean (y-axis) are captured from input space.

Bottom Line: 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.Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network.Experiments illustrate the effectiveness of the proposed method.

View Article: PubMed Central - PubMed

Affiliation: Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P. R. China.

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.

No MeSH data available.