  Signal classification problems arise in a wide variety of applications, and
their demand is only expected to grow. In this paper, we focus on the wireless
sensor network signal classification setting, where each sensor forwards
quantized signals to a fusion center to be classified. Our primary goal is to
train a decision function and quantizers across the sensors to maximize the
classification performance in an online manner. Moreover, we are interested in
sparse sensor selection using a marginalized weighted kernel approach to
improve network resource efficiency by disabling less reliable sensors with
minimal effect on classification performance.To achieve our goals, we develop a
multi-sensor online kernel scalar quantization (MSOKSQ) learning strategy that
operates on the sensor outputs at the fusion center. Our theoretical analysis
reveals how the proposed algorithm affects the quantizers across the sensors.
Additionally, we provide a convergence analysis of our online learning approach
by studying its relationship to batch learning. We conduct numerical studies
under different classification and sensor network settings which demonstrate
the accuracy gains from optimizing different components of MSOKSQ and
robustness to reduction in the number of sensors selected.
