TY - JFULL
AU - Nileshkumar Vaishnav and Aditya Tatu
PY - 2017/4/
TI - Efficient Filtering of Graph Based Data Using Graph Partitioning
T2 - International Journal of Information and Communication Engineering
SP - 398
EP - 402
VL - 11
SN - 1307-6892
UR - https://publications.waset.org/pdf/10006802
PU - World Academy of Science, Engineering and Technology
NX - Open Science Index 123, 2017
N2 - An algebraic framework for processing graph signals
axiomatically designates the graph adjacency matrix as the shift
operator. In this setup, we often encounter a problem wherein we
know the filtered output and the filter coefficients, and need to
find out the input graph signal. Solution to this problem using
direct approach requires O(N3) operations, where N is the number
of vertices in graph. In this paper, we adapt the spectral graph
partitioning method for partitioning of graphs and use it to reduce
the computational cost of the filtering problem. We use the example
of denoising of the temperature data to illustrate the efficacy of the
approach.
ER -