Scalable learning for restricted Boltzmann machines
We propose Eigen-RBM, a scalable Restricted Boltzmann Machine (RBM) for visual recognition in which the number of free parameters to learn is independent of the image size. Eigen-RBM exploits the global structure of the image and does not impose any locality or translation-invariance assumption, and regularizes the network weights to be a linear combination of a set of predefined filters. We show that, compared to basic RBM, Eigen-RBM can achieve similar or better performance in both recognition and sample generation with significantly less training time.