论文标题
基于图形的拉普拉斯矩阵的光谱的增强离群检测方法
A boosted outlier detection method based on the spectrum of the Laplacian matrix of a graph
论文作者
论文摘要
本文探讨了一种基于图的拉普拉斯矩阵的光谱的新离群值检测算法。利用与稀疏数据的学习者一起提升。拉普拉斯矩阵的疏松大大减轻了计算负担，与光谱聚类相比，可以将基于频谱的离群检测方法应用于较大的数据集。该方法具有具有常用的离群检测算法（如隔离林和局部离群因素）的合成数据集竞争性。
This paper explores a new outlier detection algorithm based on the spectrum of the Laplacian matrix of a graph. Taking advantage of boosting together with sparse-data based learners. The sparcity of the Laplacian matrix significantly decreases the computational burden, enabling a spectrum based outlier detection method to be applied to larger datasets compared to spectral clustering. The method is competitive on synthetic datasets with commonly used outlier detection algorithms like Isolation Forest and Local Outlier Factor.
