TY - GEN
T1 - Accelerated algorithms for Eigen-Value Decomposition with application to spectral clustering
AU - Lu, Songtao
AU - Wang, Zhengdao
N1 - Publisher Copyright:
© 2015 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016/2/26
Y1 - 2016/2/26
N2 - Fast and accurate numerical algorithms for Eigen-Value Decomposition (EVD) are of great importance in solving many engineering problems. In this paper, we aim to develop algorithms for finding the leading eigen pairs with improved convergence speed compared to existing methods. We introduce several accelerated methods based on the power iterations where the main modification is to introduce a memory term in the iteration, similar to Nesterov's acceleration. Results on convergence and the speed of convergence are presented on a proposed method termed Memory-based Accelerated Power with Scaling (MAPS). Nesterov's acceleration for the power iteration is also presented. We discuss possible application of the proposed algorithm to (distributed) clustering problems based on spectral clustering. Simulation results show that the proposed algorithms enjoy faster convergence rates than the power method for matrix eigen-decomposition problems.
AB - Fast and accurate numerical algorithms for Eigen-Value Decomposition (EVD) are of great importance in solving many engineering problems. In this paper, we aim to develop algorithms for finding the leading eigen pairs with improved convergence speed compared to existing methods. We introduce several accelerated methods based on the power iterations where the main modification is to introduce a memory term in the iteration, similar to Nesterov's acceleration. Results on convergence and the speed of convergence are presented on a proposed method termed Memory-based Accelerated Power with Scaling (MAPS). Nesterov's acceleration for the power iteration is also presented. We discuss possible application of the proposed algorithm to (distributed) clustering problems based on spectral clustering. Simulation results show that the proposed algorithms enjoy faster convergence rates than the power method for matrix eigen-decomposition problems.
KW - Eigen-value decomposition
KW - Nesterov acceleration
KW - distributed implementation
KW - spectral clustering
UR - http://www.scopus.com/inward/record.url?scp=84969776486&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84969776486&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2015.7421146
DO - 10.1109/ACSSC.2015.7421146
M3 - Conference contribution
AN - SCOPUS:84969776486
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 355
EP - 359
BT - Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Y2 - 8 November 2015 through 11 November 2015
ER -