让数据可视化同时保留局部与全局结构，提升准确性。
StarMAP: Global Neighbor Embedding for Faithful Data Visualization
- 利用PCA的全局投影特性构建星状吸引力机制
- 在多种数据集上实现更忠实的全局结构保留
- 兼顾可解释性与计算效率，适合生物与深度学习应用
邻域嵌入广泛用于高维数据可视化，但常忽略全局结构（如聚类间相似性），影响可视化准确性。本文提出星吸引流形近似与投影（StarMAP），融合主成分分析（PCA）在邻域嵌入中的优势。受PCA嵌入可视为数据最大投影的启发，StarMAP引入‘星吸引’概念，利用PCA嵌入引导全局结构保持。该方法在保持邻域嵌入可解释性与计算效率的同时，显著提升全局结构保真度。在合成数据、单细胞RNA测序数据及深度表示的可视化任务中，与现有方法对比显示，StarMAP虽简单却有效，能实现更忠实的数据可视化。
原文摘要 · Abstract (English)
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.
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