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

## TL;DR

The paper introduces a new online dimensionality reduction method that improves data topology representation and handles large datasets effectively.

## Contribution

The novel method combines an improved Topology Representing Network with a Radial Basis Function Network for online dimensionality reduction.

## Key findings

- The method can process nonlinear embedded manifolds and map new data online.
- It effectively handles large datasets due to the improved Topology Representing Network.
- Experiments show the proposed method is effective for finding low-dimensional feature structures.

## 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.