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Compressive Classification Based on One-Bit Measurements

Classification is an important task in the fields of signal processing and machine learning. Compressive classification (CC), which exploits the technique of compressive sensing (CS), appears recently that directly classifies signals in the compressed domain. The existing works have shown that compressive classification can achieve reliable classification based on few compressive measurements. To further reduce the complexity, this paper studies the one-bit CC based on one-bit CS, which only acquires the sign information of compressive measurements, and thus is appealing in many applications when the bandwidth and energy are constrained. When the sources follow Gaussian mixture model, we derive the upper bound of the probability of misclassification for one-bit CC. We also propose a practical one-bit CC scheme and show its promising performance by experiments with both synthetic data and real image data.