StarMAP: Global Neighbor Embedding for Faithful Data Visualization
This addresses the need for more faithful visualizations in fields like bioinformatics and machine learning, though it is incremental as it builds on existing neighbor embedding and PCA techniques.
The paper tackled the problem of neighbor embedding methods often overlooking global structure in data visualization, and introduced StarMAP, which incorporates PCA to preserve intercluster similarities effectively, showing it is simple and effective in experiments on toy datasets, single-cell RNA sequencing data, and deep representations.
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.