  We investigate the potential of a restricted Boltzmann Machine (RBM) for
discriminative representation learning. By imposing the class information
preservation constraints on the hidden layer of the RBM, we propose a Signed
Laplacian Restricted Boltzmann Machine (SLRBM) for supervised discriminative
representation learning. The model utilizes the label information and preserves
the global data locality of data points simultaneously. Experimental results on
the benchmark data set show the effectiveness of our method.
