StarMAP: Global Neighbor Embedding for Faithful Data Visualization

TL;DR

StarMAP is a novel neighbor embedding technique that effectively preserves global data structure, combining PCA insights with neighbor embedding for more faithful and interpretable visualizations of high-dimensional data.

Contribution

StarMAP introduces the concept of star attraction, integrating PCA with neighbor embedding to improve global structure preservation in data visualization.

Findings

StarMAP outperforms existing methods in global structure preservation.

It maintains interpretability and computational efficiency.

Effective across various datasets including single-cell RNA sequencing.

Abstract

Neighbor embedding is widely employed to visualize high-dimensional data; however, it frequently overlooks the global structure, e.g., intercluster similarities, thereby impeding accurate visualization. To address this problem, this paper presents Star-attracted Manifold Approximation and Projection (StarMAP), which incorporates the advantage of principal component analysis (PCA) in neighbor embedding. Inspired by the property of PCA embedding, which can be viewed as the largest shadow of the data, StarMAP introduces the concept of \textit{star attraction} by leveraging the PCA embedding. This approach yields faithful global structure preservation while maintaining the interpretability and computational efficiency of neighbor embedding. StarMAP was compared with existing methods in the visualization tasks of toy datasets, single-cell RNA sequencing data, and deep representation. The experimental results show that StarMAP is simple but effective in realizing faithful visualizations.