Joint Eigenvalue Decomposition Using Polar Matrix Factorization

被引：17

作者：

Luciani, Xavier ^{[1
]}

Albera, Laurent ^{[1
]}

机构：

[1] INSERM, UMR 642, F-35000 Rennes, France

来源：

LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION | 2010年 / 6365卷

关键词：

Joint diagonalization by similarity; joint eigenvalue decomposition; Jacobi method; polar matrix decomposition; BLIND SOURCE SEPARATION; JACOBI-LIKE METHOD; SIMULTANEOUS DIAGONALIZATION; NONDEFECTIVE MATRICES; COMPUTATION; ESPRIT;

D O I：

10.1007/978-3-642-15995-4_69

中图分类号：

TP301 [理论、方法];

学科分类号：

080201 [机械制造及其自动化];

摘要：

In this paper we propose a new algorithm for the joint eigen-value decomposition of a set of real non-defective matrices. Our approach resorts to a Jacobi-like procedure based on polar matrix decomposition. We introduce a new criterion in this context for the optimization of the hyperbolic matrices, giving birth to an original algorithm called JDTM. This algorithm is described in detail and a comparison study with reference algorithms is performed. Comparison results show that our approach provides quicker and more accurate results in all the considered situations.

引用

页码：555 / 562

页数：8

共 16 条

[1]

ICAR:: A tool for blind source separation using fourth-order statistics only [J].