English(EN) Unsupervised Feature Selection Using Local Intrinsic Dimensionality -- Extended Version

新的无监督特征选择方法 LIDFS 优于现有技术

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了 LIDFS，一种新颖的无监督特征选择方法，它利用局部内在维度 (LID) 来识别高维数据集中最具区分性的特征。该方法将特征视为点，并根据其最近邻估计其 LID，将较低 LID 的特征分配更高的重要性。LIDFS 不需要类标签，时间复杂度为 O(d^2n)。在 23 个数据集上的评估表明，LIDFS 在监督任务中的表现优于现有方法，并且在聚类评估中表现具有竞争力，即使与使用类标签的方法相比也是如此。 AI

影响该方法可以通过在不需要标记数据的情况下降低维度来提高机器学习算法的效率和有效性。

排序理由该集群包含一篇详细介绍一种新的无监督特征选择方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Oussama Chelly, Michael E. Houle, Ken-ichi Kawarabayashi, Muhammad Rajabinasab, Arthur Zimek · 2026-11-02 05:00

Unsupervised Feature Selection Using Local Intrinsic Dimensionality -- Extended Version

arXiv:2610.40736v1 Announce Type: new Abstract: High dimensionality degrades both the efficiency and the effectiveness of learning algorithms, and in many applications no class labels are available to guide dimensionality reduction. This paper proposes LIDFS, an unsupervised feat…

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Unsupervised Feature Selection Using Local Intrinsic Dimensionality -- Extended Version

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