Visualizing large-scale high-dimensional data via hierarchical embedding of KNN graphs

Abstract

Visualizing intrinsic structures of high-dimensional data is an essential task in data analysis. Over the past decades, a large number of methods have been proposed. Among all solutions, one promising way for enabling effective visual exploration is to construct a k-nearest neighbor (KNN) graph and visualize the graph in a low-dimensional space. Yet, state-of-the-art methods such as the LargeVis still suffer from two main problems when applied to large-scale data: (1) they may produce unappealing visualizations due to the non-convexity of the cost function; (2) visualizing the KNN graph is still time-consuming. In this work, we propose a novel visualization algorithm that leverages a multi-level representation to achieve a high-quality graph layout and employs a cluster-based approximation scheme to accelerate the KNN graph layout. Experiments on various large-scale datasets indicate that our approach achieves a speedup by a factor of five for KNN graph visualization compared to LargeVis and yields aesthetically pleasing visualization results.