AI 中文总结
本研究提出与降维（DR）方法无关的FADEx方法，通过局部线性近似等技术生成逐实例局部特征归因与失真分析，其解释鲁棒可靠，在多方面优于现有DR解释方法。
AI 中文摘要
降维（Dimensionality Reduction，DR）是用于高维数据探索的基础工具，可降低机器学习模型潜在空间的复杂度，并辅助解释复杂不透明模型。然而，非线性DR技术本身常作为不透明变换，难以理解单个特征如何影响降维空间中的实例定位；这种透明度缺失会增加结构模式的分析与解释难度，阻碍基于投影布局推理高维数据组织的能力。为解决该挑战，降维解释方法有望提升对观测群组与聚类结构的理解，但现有DR解释方法存在每个特征对应多个归因、仅适用于特定DR方法等局限性，限制了其应用。本研究提出FADEx，一种新颖的逐实例局部特征归因方法，通过一阶泰勒展开与奇异值分解（Singular Value Decomposition）利用局部线性近似生成解释；FADEx通过加权最小二乘法计算局部线性模型，无需样本外数据映射，因此与DR方法无关，同时可提供局部特征归因与失真分析。通过定性与定量评估、与现有方法的对比及案例研究，证明FADEx在为分析DR方法行为提供解释与分析资源方面的有效性与通用性，结果表明FADEx生成的解释鲁棒且可靠，在多个方面优于现有方法。
英文摘要
Dimensionality Reduction (DR) is a fundamental tool for high-dimensional data exploration, reducing the complexity of latent spaces of machine learning models, and assisting in the explanation of complex opaque models. However, non-linear DR techniques often function as opaque transformations themselves, making it challenging to understand how individual features influence instance positioning in the reduced space. This lack of transparency complicates the analysis and interpretation of structural patterns, hindering the ability to reason about the organization of high-dimensional data based on the projected layout. In order to address this challenge, dimensionality reduction explanation methods have shown promise in improving the understanding of the observed groups and cluster structures. Unfortunately, existing DR explanation approaches tend to suffer from limitations such as multiple attributions per feature and restricted applicability to specific dimensionality reduction methods, which hinder their use. In this work, we propose FADEx, a novel local per-instance feature attribution method that leverages local linear approximation via first-order Taylor expansion and Singular Value Decomposition to provide explanations. FADEx computes the local linear models via weighted least squares, eliminating the need for out-of-sample data mapping, making it agnostic to the DR method, while simultaneously providing local feature attributions and distortion analysis. Through qualitative and quantitative evaluations, comparisons with existing methods, and case studies, we demonstrate FADEx's effectiveness and versatility in providing explanations and analytical resources for analyzing the behavior of DR methods. The results indicate FADEx yields robust and reliable explanations, outperforming existing approaches in several aspects.
Comments18 pages, 17 figures, to be published in IEEE Transactions on Visualization and Computer Graphics
