论文标题
角嵌入：一种新的角度鲁棒主成分分析
Angular Embedding: A New Angular Robust Principal Component Analysis
论文作者
论文摘要
作为机器学习中广泛使用的方法，主成分分析（PCA）显示了降低维度的出色特性。 PCA对离群值敏感是一个严重的问题，该异常值已通过众多可靠的PCA（RPCA）版本改善。但是，现有的最先进的RPCA方法无法轻易通过非著作方式删除或容忍异常值。为了解决此问题，本文提出了角度嵌入（AE），以基于角度密度制定直接的RPCA方法，该方法可以改进大规模或高维数据。此外，还引入了修剪的AE（TAE）来处理大规模异常值的数据。具有矢量级别或像素级别离群值的合成数据集和现实世界数据集的广泛实验表明，所提出的AE/TAE的表现优于基于最新的RPCA方法。
As a widely used method in machine learning, principal component analysis (PCA) shows excellent properties for dimensionality reduction. It is a serious problem that PCA is sensitive to outliers, which has been improved by numerous Robust PCA (RPCA) versions. However, the existing state-of-the-art RPCA approaches cannot easily remove or tolerate outliers by a non-iterative manner. To tackle this issue, this paper proposes Angular Embedding (AE) to formulate a straightforward RPCA approach based on angular density, which is improved for large scale or high-dimensional data. Furthermore, a trimmed AE (TAE) is introduced to deal with data with large scale outliers. Extensive experiments on both synthetic and real-world datasets with vector-level or pixel-level outliers demonstrate that the proposed AE/TAE outperforms the state-of-the-art RPCA based methods.
