论文标题
光谱聚类中的特征选择：理论指导实践
Eigen selection in spectral clustering: a theory guided practice
论文作者
论文摘要
基于高斯混合物类型模型，我们得出了一个特征选择程序，该过程改善了高维设置中通常的光谱聚类。具体而言，我们得出了在特征值多样性和特征值比率浓度结果下的尖峰特征值的渐近膨胀结果，从而在光谱群集中引起了第一个理论支持的特征原理选择程序。由此产生的欧吉人选择的光谱聚类（ESSC）算法具有更好的稳定性，并且可以与规范替代方案进行比较。我们使用广泛的模拟和多个实际数据研究证明了ESSC的优势。
Based on a Gaussian mixture type model , we derive an eigen selection procedure that improves the usual spectral clustering in high-dimensional settings. Concretely, we derive the asymptotic expansion of the spiked eigenvalues under eigenvalue multiplicity and eigenvalue ratio concentration results, giving rise to the first theory-backed eigen selection procedure in spectral clustering. The resulting eigen-selected spectral clustering (ESSC) algorithm enjoys better stability and compares favorably against canonical alternatives. We demonstrate the advantages of ESSC using extensive simulation and multiple real data studies.
