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Local linear embedding proposed that face data would found in some low dimensional subspace, All face data would be linearity denoted optimal with data in neighborhood of the data. The input space without linear separability be mapped into linear divisible high dimensional space by nonlinear map-ping. Structure kernel spread inner matrix based on local linear embedding and kernel fisher nonlinear...
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