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It is known that an Restricted Boltzmann machine (RBM) can be used as a feature extractor to automatically extract data features in a completely unsupervised learning manner. In this paper, we develop a new regularized RBM by adding the class information, referred to as class preserving RBM (CPr-RBM). Specifically, we impose two constraints on RBM to make the class information clearly reflected in...
Restricted Boltzmann machines (RBMs) are often used as building blocks to construct a deep belief network. By optimizing several RBMs, the deep networks can be trained quickly to achieve good performance on the tasks of interest. To further improve the performance of data representation, many researches focus on incorporating sparsity into RBMs. In this paper, we propose a novel sparse RBM model,...
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