Title :
Graph signal coarsening: Dimensionality reduction in irregular domain
Author :
Pengfei Liu ; Xiaohan Wang ; Yuantao Gu
Author_Institution :
Dept. of Electron. Eng., Tsinghua Univ., Beijing, China
Abstract :
Graph signal coarsening is a kind of dimensionality reduction in irregular domain. Given a graph signal, it aims to simultaneously obtain a coarser version of the graph and a coarsened signal on the new graph. In this work, we explore the design space for the graph signal coarsening problem and show that solutions can be split into four categories. We propose an effective method that uses a successive approach and spectral-domain-based signal coarsening for solving the problem, which is the first that falls into one of the four categories. Experiments are conducted to show the effectiveness of the proposed method.
Keywords :
graph theory; signal processing; dimensionality reduction; graph signal coarsening problem; irregular domain; spectral-domain-based signal coarsening; Approximation algorithms; Communities; Eigenvalues and eigenfunctions; Laplace equations; Space exploration; Spectral analysis; dimensionality reduction; graph signal coarsening; graph signal processing; irregular domain; spectral graph theory;
Conference_Titel :
Signal and Information Processing (GlobalSIP), 2014 IEEE Global Conference on
Conference_Location :
Atlanta, GA
DOI :
10.1109/GlobalSIP.2014.7032229