Simple Classification Using Binary Data
Deanna Needell, Rayan Saab, Tina Woolf; 19(61):1−30, 2018.
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 obtained from the sign pattern of low-dimensional projections 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.
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