基于对维度灾难的鲁棒性进行特征选择
Selecting Features by their Resilience to the Curse of Dimensionality
AI总结：
本文提出一种利用内在维度识别能区分不同规模数据子集的特征选择方法，以削弱维度灾难，实验表明其优于现有方法并可扩展到百万级数据集。
摘要：
现实世界的数据集往往具有高维度，并受到维度灾难的影响，这阻碍了它们的可理解性和可解释性。为了降低复杂性，特征选择旨在识别对从所述数据中学习至关重要的特征。虽然相关性度量和成对相似性度量被广泛使用，但维度灾难很少被纳入特征选择过程。在此，我们提出一种新方法，用于识别能够区分不同规模数据子集的特征。通过借鉴最近关于计算内在维度的工作，我们的方法能够选择能够区分数据、从而削弱维度灾难的特征。我们的实验表明，该方法具有竞争力，并且通常优于已有的特征选择方法。此外，我们提出一种近似方案，使我们的方法能够扩展到包含数百万数据点的数据集。我们的研究结果表明，能够区分数据且与低内在维度相关的特征对学习过程是有意义的。
英文摘要：
Real-world datasets are often of high dimension and effected by the curse of dimensionality. This hinders their comprehensibility and interpretability. To reduce the complexity feature selection aims to identify features that are crucial to learn from said data. While measures of relevance and pairwise similarities are commonly used, the curse of dimensionality is rarely incorporated into the process of selecting features. Here we step in with a novel method that identifies the features that allow to discriminate data subsets of different sizes. By adapting recent work on computing intrinsic dimensionalities, our method is able to select the features that can discriminate data and thus weaken the curse of dimensionality. Our experiments show that our method is competitive and commonly outperforms established feature selection methods. Furthermore, we propose an approximation that allows our method to scale to datasets consisting of millions of data points. Our findings suggest that features that discriminate data and are connected to a low intrinsic dimensionality are meaningful for learning procedures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
