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In the real world, data samples are often contaminated. Using these contaminated data samples for subspace segmentation usually leads to segmentation results distorted. To remove error, many existing subspace segmentation algorithms directly use different regularization to model the error of the corresponding type in the objective. However, the priori of errors is difficult to obtain in practice,...
This work proposes a new formulation for supervised stacked autoencoder. We argue that features from the same class should be similar to each other and hence linearly dependent. This means that, when stacked as columns, the feature matrix for each class will be rank deficient (low-rank). We impose this constraint into the stacked autoencoder formulation in the form of nuclear norm penalties on class-wise...
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