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SPP-CGE The affinity matrix is obtained by sparse representation as showed in Eq. (11). In this way, the affinity matrix can be obtained without setting any parameters. We also want to keep the sparse representation in the projection space. e. let y = Uz. Then, Eq. (22) becomes: (23) where the i-th item of ei is 1, 0 otherwise. With simple algebraic manipulations, the objective function can be rewritten as: (24) where = S + ST- ST S. With the constraint zT UT Uz = 1, the objective function Eq. (24) can be re-casted into: (25) The optimal vector z is given by the maximum eigenvalue solution to the following generalized eigenvalue problem: (26) The eigenvectors z1, .

Similar to Table 5, the best accuracy rate did not appear in the case of using features from the deepest layer but from a middle layer. g. Gabor features in the 1st layer). However, low order features can be hardly separated than higher ones, so they are not the best choice. On the contrary, a layer close to the output layer contains high order features which may have functions for detecting specific targets. High order features are useful for specific problems, and cannot be generalized to other problems easily.

Three types of feature generalization were examined on similar problems with different classifiers, different data or different categories. These experiments confirmed the validity of the CNN feature extractors. g. Using recurrent networks to solve the classification problem on image sequences. It will make better use of the information in the time domain. Optimizing network architecture and training parameters by hyperparameter optimization techniques. Localizing and classifying several facial expressions in one image simultaneously, depending on the multiple target detection function of CNNs.

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