加权 t-SNE 评估高维数据集上特征打分器的结果
Assessing feature scorer results on high-dimensional datasets with t-SNE
AI 导读
这项研究提出 weighted t-SNE，通过修改二维嵌入空间中数据点的关系来反映特征打分器（feature scorer）对原始数据各维度重要性的评估。
正文 · 原文
Abstract:While vast literature on high-dimensional data visualization is available, there are not many works regarding the visualization of feature scorers and their results. Feature scorers are algorithms that assign numerical importance to each feature of multi-dimensional datasets. These importance scores can be used in several applications, such as feature selection, knowledge discovery, and machine learning interpretability. There are several feature scorers to choose from, and often no single metric or ground truth is available to guarantee the quality of their results. In this scenario, visualization can become valuable to support the decision of which method to choose and how good its results are. For this goal, this work presents "weighted t-SNE." It modifies the relationship between data points in the embedded 2D space to reflect the importance of each dimension of the original datasets as assessed by a feature scorer. This research discusses how to implement weighted t-SNE, proposes the silhouette coefficient as a numerical evaluation of the results, and shows several examples of its use in practice. Synthetic and real-world tabular datasets are used in the experiments together with nine feature scorers, ranging from Mutual Information to neural networks. Each feature scorer produces unique visualizations, and weighted t-SNE can be used to compare and choose the one that better suits a given dataset and task. Weighted t-SNE can also visually show the importance of features learned by machine learning models and help us see how they are organizing the data, increasing their interpretability.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.6; I.5.3 |
| Cite as: | arXiv:2610.39009 [cs.LG] |
| (or arXiv:2610.39009v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39009 arXiv-issued DOI via DataCite |
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| Journal reference: | B.I. Grisci, M. Inostroza-Ponta, M. Dorn, Assessing feature scorer results on high-dimensional datasets with t-SNE, Neurocomputing 652 (2025) 130561 |
| Related DOI: | https://doi.org/10.1016/j.neucom.2025.130561
DOI(s) linking to related resources |
Submission history
From: Bruno Iochins Grisci [view email]
[v1]
Wed, 28 Oct 2026 22:40:13 UTC (4,108 KB)
来源：arXiv cs.AI · arxiv.org