级联子空间聚类用于离群点检测
Cascade Subspace Clustering for Outlier Detection
AI总结：
本文提出一种基于级联子空间聚类的离群点检测框架，通过迭代构建多遍自表示并结合马尔可夫链，将弱检测器组合为强检测器，在图像和说话人数据集上优于现有方法。
摘要：
许多基于稀疏和低秩表示的方法已经发展起来，并保证了正确离群点检测。自表示指出，子空间中的一个点总是可以表示为该子空间中其他点的线性组合。可以在自表示上定义一个合适的马尔可夫链，它使我们能够识别内点和离群点之间的差异。然而，自表示的重构误差（对离群点检测仍然具有信息量）却被忽略了。受梯度提升的启发，在本文中，我们提出了一种新的离群点检测框架，该框架通过构建多遍自表示，以迭代方式将一系列弱的“离群点检测器”组合成一个强的检测器。在每个阶段，我们基于弹性网络构建一个自表示，并定义一个合适的马尔可夫链来检测离群点。自表示的残差用于下一阶段以学习下一个较弱的离群点检测器。这样的阶段将重复多次。最终的离群点决策由之前的所有结果生成。在图像和说话人数据集上的实验结果表明，该方法相对于最先进的稀疏和低秩离群点检测方法具有优越性。
英文摘要：
Many methods based on sparse and low-rank representation been developed along with guarantees of correct outlier detection. Self-representation states that a point in a subspace can always be expressed as a linear combination of other points in the subspace. A suitable Markov Chain can be defined on the self-representation and it allows us to recognize the difference between inliers and outliers. However, the reconstruction error of self-representation that is still informative to detect outlier detection, is neglected.Inspired by the gradient boosting, in this paper, we propose a new outlier detection framework that combines a series of weak "outlier detectors" into a single strong one in an iterative fashion by constructing multi-pass self-representation. At each stage, we construct a self-representation based on elastic-net and define a suitable Markov Chain on it to detect outliers. The residual of the self-representation is used for the next stage to learn the next weaker outlier detector. Such a stage will repeat many times. And the final decision of outliers is generated by the previous all results. Experimental results on image and speaker datasets demonstrate its superiority with respect to state-of-the-art sparse and low-rank outlier detection methods.
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。
