3 accepted papers
Kernel Regression over Graphs (KRG) was recently proposed for predicting graph signals in a supervised learning setting, where the inputs are agnostic to the graph. KRG model predicts targets that are smooth graph signals as over the given graph, given the input when all the signals are deterministi…
We develop a multi-kernel based regression method for graph signal processing where the target signal is assumed to be smooth over a graph. In multi-kernel regression, an effective kernel function is expressed as a linear combination of many basis kernel functions. We estimate the linear weights to…
We investigate the range estimate between two wireless nodes without time stamps exchanging. Considering practical aspects of oscillator clocks, we propose a new model for ranging in which the measurement errors include the sum of two distributions, namely, uniform and Gaussian. We then derive an ap…