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• W2034146151 abstract "Herein we describe theory and algorithms for detecting covariance structures in large, noisy data sets. Our work uses ideas from matrix completion and robust principal component analysis to detect the presence of low-rank covariance matrices, even when the data is noisy, distorted by large corruptions, and only partially observed. In fact, the ability to handle partial observations combined with ideas from randomized algorithms for matrix decomposition enables us to produce asymptotically fast algorithms. Herein we will provide numerical demonstrations of the methods and their convergence properties. While such methods have applicability to many problems, including mathematical finance, crime analysis, and other large-scale sensor fusion problems, our inspiration arises from applying these methods in the context of cyber network intrusion detection." @default.
• W2034146151 created "2016-06-24" @default.
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• W2034146151 date "2013-09-30" @default.
• W2034146151 modified "2023-09-23" @default.
• W2034146151 title "On covariance structure in noisy, big data" @default.
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• W2034146151 doi "https://doi.org/10.1117/12.2037882" @default.
• W2034146151 hasPublicationYear "2013" @default.
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