Ensemble-Based Manifold Learning for Visualization

Manifold learning is helpful to the discovery of the intrinsic distribution and geometry structure of data. Current manifold learning algorithms are usually sensitive to noise and input parameters. The appearance of noise and the change of input parameters usually produce significantly different learning results. In this paper, a new method is proposed based on the manifold learning algorithm Isomap through introducing ensemble learning technique, which enlarges the value range that the input parameters can take to generate good visualization effect and reduces the sensitivity to noise.