Projection-pursuit-based dimension reduction for visualization of text features
基于投影寻踪降维的文本特征可视化

Using genetic algorithm to search for the optimal projecting direction, projection pursuit model was used to project text feature data from high-dimensional space into low-dimensional space (2 or 3 dimensions ), and the linear and non-linear structures and features of the high-dimensional data were shown by its projecting feature value in the low dimensional space, therefore dimensionality was reduced and visualization for high-dimensional text feature data was realized. This method is not only cutting down the computing complexity in the process of text mining, but also helping to determine the number of initial center point for K-means algorithm, and improving the accuracy of the algorithm. Experiments demonstrate the efficiency of this method for text feature dimension reduction.