A dictionary based generalization of robust PCA

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2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Page: 1315-1319

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Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
Institute of Electrical and Electronics Engineers (IEEE)
Computer Science
conference paper description
We analyze the decomposition of a data matrix, assumed to be a superposition of a low-rank component and a component which is sparse in a known dictionary, using a convex demixing method. We provide a unified analysis, encompassing both undercomplete and overcomplete dictionary cases, and show that the constituent components can be successfully recovered under some relatively mild assumptions up to a certain global sparsity level. Further, we corroborate our theoretical results by presenting empirical evaluations in terms of phase transitions in rank and sparsity for various dictionary sizes.

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