In this study, we computationally scanned the porosity of 74 porous organic cage molecules to generate three-dimensional images, with the cavity in the center of the image. The flattened image serves as a raw vector representation of the intrinsic porosity of a cage that lies in a very high-dimensional space. We postulated that the 3D porosity images effectively lie in a much lower dimensional subspace of this enormous space. Using the singular value decomposition, we learned in an unsupervised manner the effective lower-dimensional subspace in which the void spaces of porous organic cages sit, which is characterized by a set of eigencages. Expressing a cage as a linear combination of eigencages defined its latent representation, which lent a notion of similarity between two cages. We embedded the latent representations of the porous organic cage molecules into two-dimensional space to visualize the clusters of cages. We found that the clusters in the learned latent space coincide with our intuition of cages exhibiting similarly-shaped cavities and that cages can be visually reconstructed from their low-dimensional latent representation. Furthermore, cages nearby in latent space are more likely to exhibit similar simulated Xe/Kr selectivities, cavity diameters, and number of windows entering the cavity. Together, this shows that our ν = 22-dimensional latent representation efficiently encodes the salient features of the cavities in the porous cages displayed in Fig. .