  Binary, or one-bit, representations of data arise naturally in many
applications, and are appealing in both hardware implementations and algorithm
design. In this work, we study the problem of data classification from binary
data and propose a framework with low computation and resource costs. We
illustrate the utility of the proposed approach through stylized and realistic
numerical experiments, and provide a theoretical analysis for a simple case. We
hope that our framework and analysis will serve as a foundation for studying
similar types of approaches.
