1 research outputs found
Recursive Prediction of Graph Signals with Incoming Nodes
Kernel and linear regression have been recently explored in the prediction of
graph signals as the output, given arbitrary input signals that are agnostic to
the graph. In many real-world problems, the graph expands over time as new
nodes get introduced. Keeping this premise in mind, we propose a method to
recursively obtain the optimal prediction or regression coefficients for the
recently propose Linear Regression over Graphs (LRG), as the graph expands with
incoming nodes. This comes as a natural consequence of the structure C(W)= of
the regression problem, and obviates the need to solve a new regression problem
each time a new node is added. Experiments with real-world graph signals show
that our approach results in good prediction performance which tends to be
close to that obtained from knowing the entire graph apriori