.png){kind=icon}
返回
Nonnegative Joint Diagonalization by Congruence Based on LU Matrix Factorization
.png){kind=icon}
OA
AI
DOI：10.1109/LSP.2013.2267797.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this letter, a new algorithm for joint diagonalization of a set of matrices by congruence is proposed to compute the nonnegative joint diagonalizer. The nonnegativity constraint is imposed by means of a square change of variables. Then we formulate the high-dimensional optimization problem into several sequential polynomial subproblems using LU matrix factorization. Numerical experiments on simulated matrices emphasize the advantages of the proposed method, especially in the case of degeneracies such as for low SNR values and a small number of matrices. An illustration of blind separation of nuclear magnetic resonance spectroscopy confirms the validity and improvement of the proposed method.
Keyword:
Blind source separation
independent component analysis
LU factorization
nonnegative joint diagonalization by congruence
nuclear magnetic resonance spectroscopy
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
引用论文
Expression of Tankyrase 1 in Gastric Cancer and Its Correlation with Telomerase ActivityTankyrase 1在胃癌中的表达及其与端粒酶活性的相关性
.png){kind=icon}
PREAI
分享收藏Correlation Involving Compressive Strength and Flexural Strength of Polyester Fiber-Reinforced Binary Blended Concrete涉及聚酯纤维增强二元混合混凝土抗压强度和抗折强度的相关性
PREAI
分享收藏Is independent component analysis appropriate for multivariate resolution in analytical chemistry?
PREAI
分享收藏分享收藏Influence of roofing materials and lead flashing on rainwater tank contamination by metals屋面材料和铅防水板对金属污染雨水箱的影响
PREAI
分享收藏
查看更多内容