Robust PCA via Dictionary Based Outlier Pursuit
Robust PCA via Dictionary Based Outlier Pursuit
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DOI：
10.1109/icassp.2018.8461593
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Xingguo Li;Jineng Ren;Sirisha Rambhatla;Yangyang Xu;Jarvis D. Haupt
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文献类型：
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作者：
Xingguo Li;Jineng Ren;Sirisha Rambhatla;Yangyang Xu;Jarvis D. Haupt
在本文中，我们研究了从大量内线中定位向量离群点的问题，特别关注了在已知基或字典中表示离群点的情况。使用凸解混公式，我们提供了可证明的保证，以精确恢复由内线和离群列的支持所跨越的空间，即使当内线的秩很高，离群值的数量是总观测值的恒定比例。在合成和高光谱成像真实数据集上的综合数值实验证明了该方法的有效性。
In this paper, we examine the problem of locating vector outliers from a large number of inliers, with a particular focus on the case where the outliers are represented in a known basis or dictionary. Using a convex demixing formulation, we provide provable guarantees for exact recovery of the space spanned by the inliers and the supports of the outlier columns, even when the rank of inliers is high and the number of outliers is a constant proportion of total observations. Comprehensive numerical experiments on both synthetic and hyper-spectral imaging real datasets demonstrate the efficiency of our proposed method.