arXiv cs.AI· Bruno Iochins Grisci, Mario Inostroza-Ponta, M\'arcio Dorn·· 14 小时前AI 评分22
用 t-SNE 评估高维数据集上的特征评分器结果
Assessing feature scorer results on high-dimensional datasets with t-SNE
AI 导读
研究提出“weighted t-SNE”，通过修改嵌入 2D 空间中数据点之间的关系，反映特征评分器给出的各原始维度重要性。作者给出实现方法，并提出用轮廓系数对结果做数值评估，在合成与真实表格数据集上结合从互信息到神经网络的九种特征评分器进行实验。该方法可比较并选择更适合特定数据集与任务的特征评分器，也能可视化机器学习模型所学特征的重要性。
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