Content addressable memories in reproducing Kernel Hilbert spaces
Content addressable memories (CAM) are one of the few technologies that provide the capability to store and retrieve information based on content. Even more useful is their ability to recall data from noisy or incomplete inputs. However, the input data dimension limits the amount of data that CAMs can store and successfully retrieve. We propose to increase the amount of information that can be stored by implementing CAMs in a reproducing kernel Hilbert space where the input dimension is practically infinite, effectively lifting this CAM limitation. We show the advantages of kernel CAMs over CAMs by comparing their performance in information retrieval, generalization, storage, and online learning.