An Online Kernel Scalar Quantization Scheme for Signal Classification

The number of applications requiring signal classification continues to climb, fueled at least partly by the increase in sophistication and throughput of mobile devices. One particular use case of interest is when a sensor can record samples, process the samples, and transmit this data. In this paper, we are interested in understanding the design and behavior of these relay-like classification nodes. We propose a system model consisting of a compress-and-forward relay network where the data at a given relay node is quantized and broadcasted to a fusion center which will determine a corresponding class label for the sample data using online process. In this context, we propose and study an online kernel scalar quantization learning strategy to estimate the decision function and associated empirical conditional probabilities to enhance the overall classification accuracy rate. In doing so, we devise a jointly optimum classification quantization approach that can be applied in a variety of settings in signal processing, machine learning, and communications.