High-Dimensional Data Visualization Based on User Knowledge

Due to the curse of the dimensionality, high-dimensional data visualization has always been a difficult and hot problem in the field of visualization. Some of the existing works mainly use dimensionality reduction methods to generate latent dimensions and visualize the transformed data. However, these latent dimensions often have no good interpretability with user knowledge. Therefore, this paper introduces a high-dimensional data visualization method based on user knowledge. This method can derive dimensions aligned with user knowledge to reorganize data, then it uses scatter-pie plot matrix, an extension of scatter plot matrix, to visualize the reorganized data. This method enables users to explore the relationship between the known and unknown data as well as the relationship between the unknown data and the derived dimensions. The effectiveness of the method is validated by the experiments.