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Неизв.'s Advances in Face Image Analysis: Theory and Applications PDF

By Неизв.

ISBN-10: 1681081113

ISBN-13: 9781681081113

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Extra resources for Advances in Face Image Analysis: Theory and Applications

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Compared to the results of conventional approaches on the same dataset [19], ours is better. 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.

Sparsity Preserving Projection [8-11] (SPP) is an unsupervised learning method. It can be considered as an extension to NPE since the latter has similar objective function. However, SPP utilises sparse representation to obtain the affinity matrix. We extend SPP to the semi-supervised case by integrating the idea of Constrained Graph Embedding [12] (CGE). CGE tries to project the data point with the same labels into one single point in the projection space by a constraint matrix. We propose a method called sparsity preserving projection based constrained graph embedding that can combine the benefits of both SPP and CGE.

In the projection space, a constraint matrix U is used to keep the samples with a same label in one point. The definition of U is as follows: (15) where the i-th row of J is a indictor vector of xi: (16) where j = 1,. , c. An auxiliary vector z is adopted to implement the constraint: (17) with the above constraint, it is clearly to see that if xi and xj share the same label, then yi= yj. With simple algebraic formulation, we have: (18) and (19) where the affinity matrix W is simply calculated as in Eq.

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Advances in Face Image Analysis: Theory and Applications by Неизв.


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