A New Joint Diagonalization Algorithm with Application to Blind Source Separation

被引：0
作者：
Wang, Feng [1 ]
Wu, Bin [1 ]
机构：
[1] Commercial Aircraft Corp China Ltd, Beijing Aeronaut Sci & Technol Res Inst, Beijing, Peoples R China
来源：
2012 INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING (WICOM) | 2012年
关键词：
BSS; Least-squares; real symmetric matrices; steepest descent method; and conjugate gradient method;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a novel iterative algorithm for the joint diagonalization of a set of real symmetric matrices is presented. The approximate joint diagonalization of a set of matrices is an important approach in many blind source separations (BSS) applications. By applying the least-squares criterion, a classical nonlinear least-squares cost-function of the BSS problem is obtained. An explicit method for optimizing the cost function is derived and compared with other techniques in the BSS context. Both simplicity and efficiency were achieved by applying the algorithm to BSS problems.
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页数：4
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