摘要
混合因子分析是一种对具有复杂结构的多维数据建立模型的方法.提出了一种两阶段的混合因子分析算法,它们都能够使用期望-最大化算法来实现.当给定一组随机样本时,首先建立此样本概率分布的Gauss混合模型,进而再对每一个Gauss混合项进行因子分析.实例表明算法是有效的.
基金
国家自然科学基金(批准号:60073053)
参考文献6
-
1[1]Everitt B S.An Introd uction to Latent Variable Models.London:Chapman and Hall,1984
-
2[2]Dempster A P,etal.Maximum likelihood from incomplete data via the EM algorithm.Journal of the Royal Statistical Society,1977,B-39(1):1
-
3[3]Rubin D,et al.EM algorithms for ML factor analysis.Psychome-trika,1982,47(1):69
-
4[4]Hinton G E,et al.Modeling the manifolds of images of handwritten digits.IEEE Trans Neural Networks,1997,8(1):65
-
5[5]Tipping M E,et al.Mixtures of probabilistic principal component analyzers.Neural Computation,1999,11(2):443
-
6[6]Neal R M,et al.A view of the EM algorithm that justifies incremental,sparse,and other variants.In:Jordan M I,ed.Learning in Graphical Models.Norwell MA:Kluwer Academic Publishers,1998.3551)
同被引文献53
-
1钟伟才,刘静,刘芳,焦李成.二阶卡尔曼滤波分布估计算法[J].计算机学报,2004,27(9):1272-1277. 被引量:6
-
2Patel P,et al.Analysis of a simple successive interference cancellation scheme in a DS/CDMA system.IEEE JCAS,1994,12(5):796
-
3Divsalar D,Improved parallel interference cancellation for CDMA.IEEE Trans on Commun,1998,46(2):258
-
4Aazhang B,et al.Neural network for multiuser detection in CDMA communications.IEEE J Selection Areas Comm,1992,40(7):1212
-
5Pan J,et al.Construction of orthogonal multiwavelets with short se-quence via genetic algorithm.Progress in Natural Science,2000,10(4):294
-
6Chen J L,et al.Direct sequence code division multiple access based on multiwavelet packet transform.Progress in Natural Science,2001,11(9):715
-
7Honig M,et al.Blind adaptive multiuser detection.IEEE Trans on Information Theory,1995,41(4):944
-
8Wang X D.Blind multiuser detection:A Subspace approach.IEEE Trans on Information Theory,1998,44(1):91
-
9Lang T,et al.Blind identification and equalization based on second order statistics:A time domain approach.IEEE Trans on Informa-tion Theory,1994,40(3):340
-
10Michaela C.Estimation of parametric channel modelsin wireless communication.Networks,1998
引证文献3
-
1杨晔宏,李伟生,李翠霞.一种基于混合因子分析的分布估计算法[J].信息与控制,2006,35(4):448-452.
-
2焦李成,马海波,刘芳.多用户检测与独立分量分析:进展与展望[J].自然科学进展,2002,12(4):365-371. 被引量:5
-
3巴丽伟,童常青.基于变分贝叶斯推断的因子分析法[J].杭州电子科技大学学报(自然科学版),2022,42(3):95-102. 被引量:2
二级引证文献7
-
1张旭秀,王培,邱天爽.基于改进信息最大化法的盲多用户检测[J].信息与控制,2008,37(5):556-559.
-
2何峰,丁宏,郑林华.基于WINC的自适应主分量提取的盲多用户检测算法[J].信号处理,2010,26(1):7-11.
-
3靳天玉,郭明超,饶增仁.一种基于全新算法的盲最佳多用户检测器[J].甘肃科学学报,2010,22(4):110-113.
-
4刘宁,张伟涛,楼顺天,冶继民.基于模糊逻辑的亚高斯与超高斯源混合盲分离[J].系统工程与电子技术,2011,33(7):1438-1442.
-
5刘冬冬,李林才,句媛媛,吴刘仓,肖清泰.基于因子分析的卷积神经网络模型压缩算法研究[J].昆明理工大学学报(自然科学版),2024,49(2):207-214.
-
6陶言和,郭勤涛,周瑾,马嘉倩,李效法.观测不确定性下变分贝叶斯高效模型修正[J].航空学报,2024,45(19):187-201.
-
7朱孝龙,张贤达,冶继民.基于自然梯度的递归最小二乘盲信号分离[J].中国科学(E辑),2003,33(8):741-748. 被引量:15
-
1岳博,焦李成.混合因子分析的重新抽样方法[J].电子学报,2002,30(12):1873-1875. 被引量:2
-
2胡朝明,万中,王旭.一种新的非单调谱共轭梯度算法[J].数学物理学报(A辑),2013,33(1):78-88. 被引量:2
-
3解锋昌,孙宏义.稳健的t-MFA模型的极大似然估计[J].安徽工程科技学院学报(自然科学版),2005,20(4):62-66.
-
4卢宸华,刘波,唐立强.压力敏感性弹塑性材料平面应力条件下起始扩展裂纹尖端的渐近场[J].力学季刊,2011,32(3):452-459.