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Therefore, feature dimension is reduced efficiently.
where d is the feature dimension number and D is the total number of feature dimension.
Kernel principal component extraction and feature dimension reduction are performed.
Next, we reduce the feature dimension by PCA dimension reduction.
Thus, the final AMFB feature dimension is 13×9=117.
With lateral feature dimension corresponding to both angles of incidence.
In this way, the feature dimension is significantly reduced.
The first assumption is about the dependency of certain feature dimension to the other feature dimensions, and the second is about the RTF of feature dimension.
LDA was applied to reduce the feature dimension to 40, and next, MLLT was also applied.
Fig. 2 Classification accuracy versus different feature dimension on the extended Yale B dataset.
The feature dimension and the feature extraction time for four methods are listed in Table 6.
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