Visualizing the Finer Cluster Structure of Large-Scale and High-Dimensional Data

Dimension reduction and visualization of high-dimensional data have become very important research topics because of the rapid growth of large databases in data science. In this paper, we propose using a generalized sigmoid function to model the distance similarity in both high- and low-dimensional spaces. In particular, the parameter b is introduced to the generalized sigmoid function in low-dimensional space, so that we can adjust the heaviness of the function tail by changing the value of b. Using both simulated and real-world data sets, we show that our proposed method can generate visualization results comparable to those of uniform manifold approximation and projection (UMAP), which is a newly developed manifold learning technique with fast running speed, better global structure, and scalability to massive data sets. In addition, according to the purpose of the study and the data structure, we can decrease or increase the value of b to either reveal the finer cluster structure of the data or maintain the neighborhood continuity of the embedding for better visualization. Finally, we use domain knowledge to demonstrate that the finer subclusters revealed with small values of b are meaningful.