A dictionary based generalization of robust PCA

Citation data:

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

Publication Year:
2016
Captures 2
Readers 2
Citations 1
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DOI:
10.1109/globalsip.2016.7906054
Author(s):
Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
Publisher(s):
Institute of Electrical and Electronics Engineers (IEEE)
Tags:
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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