Data Dimensionality Reduction Algorithm Based on Direct Estimate Grads

The dimensionality reduction techniques are very important for high-dimensional nonlinear data to complete the processing and analysis of the high complexity data source.From the view of topology,the process of dimensionality reduction is to find a low-dimensional linear or nonlinear manifold embedded in the high-dimensional data.On the basis of locally embedding manifold learning algorithms,a data dimensionality reduction algorithm based on direct estimate grads is proposed.The dimensionality reduction of the high-dimensionality nonlinear data is achieved by locally linear error approximating minimum.The simulation results of Swiss roll curse sampling and test show the significant effectiveness of the proposed algorithm.