2016 IEEE International Conference on Consumer Electronics-China (ICCE-China) 2016
DOI: 10.1109/icce-china.2016.7849753
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Grouping singular spectrum analysis components via mixed integer quadratic programming

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“…In particular, The SSA components are grouped by utilizing the correlation measure between the IMFs and the SSA components. Besides, the integer quadratic programming is introduced to discriminate between the signal-dominated SSA components and the noise-dominated SSA components [18,19]. The objective function is to minimize the l 2 norm error between the IMFs and the grouped SSA components while the constraint is to set the number of groups being equal to the number of IMFs.…”
Section: Introductionmentioning
confidence: 99%
“…In particular, The SSA components are grouped by utilizing the correlation measure between the IMFs and the SSA components. Besides, the integer quadratic programming is introduced to discriminate between the signal-dominated SSA components and the noise-dominated SSA components [18,19]. The objective function is to minimize the l 2 norm error between the IMFs and the grouped SSA components while the constraint is to set the number of groups being equal to the number of IMFs.…”
Section: Introductionmentioning
confidence: 99%