基于多维独立成分分析的数值仿真与分析

doi:10.3724/SP.J.1087.2012.00994

计算机应用 ›› 2012, Vol. 32 ›› Issue (04): 994-998.DOI: 10.3724/SP.J.1087.2012.00994

基于多维独立成分分析的数值仿真与分析

谢永红¹,张国伟²

1. 哈尔滨金融学院计算机系, 哈尔滨 150030
2. 西安交通大学电子与信息工程学院, 西安 710049

收稿日期:2011-10-13 修回日期:2011-11-26 发布日期:2012-04-20 出版日期:2012-04-01
通讯作者: 谢永红
作者简介:谢永红(1966－), 女，广东梅县人，副教授，硕士，主要研究方向：信号处理、信息处理、数据挖掘；
张国伟(1989－)，男，重庆人，硕士研究生，主要研究方向：阵列信号处理。

Numerical simulation and analysis based on multidimensional independent component analysis

XIE Yong-hong¹,ZHANG Guo-wei²

1. Department of Computer Science, Harbin Finance University, Harbin Heilongjiang 150030, China
2. School of Electronics and Information Engineering, Xian Jiaotong University, Xian Shaanxi 710049, China

Received:2011-10-13 Revised:2011-11-26 Online:2012-04-20 Published:2012-04-01
Contact: XIE Yong-hong

摘要/Abstract

摘要： 通过引入一个用于评价多维独立成分分析(MICA)算法性能的指标，进行数值仿真来研究其分离性。将多维Amari分离误差作为度量多维独立成分分析算法性能的一个重要指标，在比较分析研究vkMICA、cfMICA、MSOBI、SJADE等四个算法的分离性能的基础上，使用随机分布生成的字母信号进行仿真与测试，直观地显示了MICA模型的分离效果和不确定性。研究结果显示，MICA是一种非常有效的进行多维源信号分析的方法。

关键词: 多维独立成分分析, 多维Amari, 数值仿真, 信号测试

Abstract: By introducing an indicator to evaluate performance of Multidimensional Independent Component Analysis (MICA) algorithm, the separation was studied by numerical simulation. The multidimensional Amari separation error was used as an important indicator of the measurement of MICA algorithm performance. In the comparative separation performance analysis of four algorithms named vkMICA, cfMICA, MSOBI, SJADE, a random distribution of letters signal was used for simulation and testing, and a visual representation of MICA model of separation and uncertainty was got. The results show that MICA is a very effective method for multidimensional source signal analysis.

Key words: Multidimensional Independent Component Analysis (MICA), multidimensional Amari, numerical simulation, signal testing

谢永红张国伟. 基于多维独立成分分析的数值仿真与分析[J]. 计算机应用, 2012, 32(04): 994-998.

XIE Yong-hong ZHANG Guo-wei. Numerical simulation and analysis based on multidimensional independent component analysis[J]. Journal of Computer Applications, 2012, 32(04): 994-998.

参考文献

[1] CARDOSO L J F. Multidimensional independent component analysis［C］// Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 1998, 4: 1941-1944. [2] SHARMA A, PALIWAL K K. Subspace independent component analysis using vector kurtois ［J］. Pattern Recognition, 2006, 39(11):2227-2232. [3] YEREDOR A. Blind source separation via the second characteristic function［J］. Signal Processing, 2000,80(5):897-902. [4] THEIS F J. Towards a general independent subspace analysis ［C］// NIPS 2006: Twentieth Annual Conference on Neural Information Processing Systems. [S.l.]: NIPS, 2006: 1-8. [5] THEIS F J. Blind signal separation into groups of dependent signals using joint block diagonalization ［C］// ISCAS 2005: IEEE International Symposium on Circuits and Systems. Kobe, Japan: [s.n.], 2005: 5878-5881. [6] 梁胜杰,张志华, 崔立林,等.基于主成分分析与核独立成分分析的降维方法「J］.系统工程与电子技术,2011,33(9):2144-2148. [7] 吴小培,冯焕清,周荷琴,等.基于独立分量分析的混合声音信号分离［J］.中国科学技术大学学报,2001,31(1):68-73. [8] 杨福生,洪波.独立分量分析的原理与应用［M］ .北京:清华大学出版社,2006. [9] CHAWLA M P S. Detection of indeterminacies in corrected ECG signals using parameterized multidimensional independent component analysis ［J］. Computational and Mathematical Methods in Medicine, 2009,10(2):85-115. [10] 赵峰. 独立成分分析改进算法与仿真研究［D］. 大连:大连交通大学, 2010. [11] 孟继成，杨万麟.独立分量分析在模式识别中的应用［J］.JOCA，2004,24(8):28-29,31. [12] 路威,张杭.基于独立成分分析的MPSK信号调制制式自动识别［J］.系统仿真学报,2008,20(7):1846-1848,1891. [13] 聂琨坤,傅彦.用ICA提取高维科学数据的特征［J］.计算机科学,2004,31(6):164-167. [14] 何元磊,刘代志,易世华,等.基于独立成分分析的高光谱图像异常检测［J］.光学技术,2011,37(2):203-207. [15] HYVARINEN A, KARHUNEN J, OJA E.独立成分分析［M］. 周宗潭，董国华，徐昕,等译.北京:电子工业出版社,2007. [16] 冯燕,何明一,宋江红,等.基于独立成分分析的高光谱图像数据降维及压缩［J］.电子与信息学报,2007,29(12): 2871-2875. [17] CHOI H, CHOI S. Relative gradient learning for independent subspace analysis ［C］// Proceedings of the 2006 International Joint Conference on Neural Networks. Washington, DC: IEEE, 2006: 3919-3924. [18] HYVARINEN A, HOYER P O. Emergence of phase and shift invariant features by decomposition of natural images into independent feafture subspaces［J］. Neural Computation,2000,12(7):1705-1720. [19] FEVOTTE C, THEIS F J. Orthonormal approximate joint block-diagonalization, GET/Telecom Paris 2007D007 ［R/OL］. [2011-06-16]. http://service.tsi.enst.fr/cgi-bin/valipub download.cgi?dId=34. [20] BACH F R, JORDAN M I. Finding clusters in independent component analysis, CSD-02-1209 ［R］. Berkeley: University of California at Berkeley, 2002. [21] ABED-MERAIM K, BELOUCHRANI A. Algorithms for joint block diagonalization ［EB/OL］. [2011-07-03]. http:// www.eurasip.org/Proceedings/Eusipco/Eusipco2004/defevent/papers/cr1926.pdf. [22] McSHARRY P, CLIFFORD G, TARASSENKO L, et al. A dynamical model for generating synthetic electrocardiogram signals ［J］. IEEE Transactions on Biomedical Engineering, 2003,50(3):289-294. [23] AKAHO S, KIUCHI Y, UMEYAMA S. MICA: Multimodal independent component analysis ［C］// IJCNN 99: International Joint Conference on Neural Networks. Piscataway: IEEE, 1999, 2: 927-932.

基于多维独立成分分析的数值仿真与分析

Numerical simulation and analysis based on multidimensional independent component analysis

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

编辑推荐

Metrics

[1]	李燕, 陈巧萍. 考虑非支持性评论的网络谣言传播模型[J]. 计算机应用, 2021, 41(4): 1128-1135.
[2]	刘军谭德庆. 供应链促销-定价决策与内生时机[J]. 计算机应用, 2013, 33(04): 971-975.