In neural networks, old classification methods usually construct a objective function to achieve dimensionality reduction, but Deep belief networks learns the data structure through unsupervised learning. How does it achieve dimensionality reduction ?

In neural networks and old classification methods, we usually construct an objective function to achieve dimensionality reduction. But Deep Belief Networks (DBN) with Restricted Boltzmann Machines (RBM) learn the data structure through unsupervised learning. How does it achieve dimensionality reduction without knowing the ground truth and constructing an objective function?