基于多源海量数据分层递阶图表示模型的可视化信息融合的研究
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摘要
本文针对海量数据处理上存在的问题和难点进行分析,提出了多源海量数据特征融合的多层次递阶结构,设计了一种处理多源海量数据的模型。此模型主要用来处理高维数据递阶、分层降维和分类决策的问题。
第一章主要介绍了海量数据处理和数据融合的背景,强调海量数据处理和数据融合在生物医学、水力部门、金融等方面的重要性。本章基于生物医学领域,阐述了医学数据的庞大和无序,急需要有专业的数据处理技术来处理海量的数据。本文的主要研究内容是提出一种海量数据的处理方法,能够从海量的数据中挖掘出隐含的信息。
第二章主要描述了海量数据的特性和对海量数据预处理的方法。海量数据一般是在超维空间中表示,而超维空间与日常中的低维空间有着明显的区别。低维空间的数据主要分布在空间的中部,而高维空间却恰恰相反,数据主要分布在边缘。所以降维的主要依据就是将数据从超维到低维的转化。数据的类型是多源的,为了将不同数据进行融合和比较,需要将不同的数据进行统一化,归一化。此过程中数据的预处理(如格式转换、归一化、降噪、平滑、降维等)就显得非常重要了。
第三章介绍了基于分层递阶图的降维与可视化信息融合方法,本章重点介绍分层递阶图的模型,海量数据降维和可视化信息融合等,可视化信息融合的方法。
第四章在前几章的理论基础上,提出了一种基于多源海量数据分层递阶图表示模型。并且将此模型运用到脑电数据的可视化处理上,得出仿真结果,说明本文提出的模型的可行性。将此模型的仿真结果与文献中已有模型的仿真结果进行对比,体现本文提出的模型的优越性。
Massive data processing in domestic and international problems and difficulties of a full analysis, this mass of data for the medicine, the main mass of data of high dimensional data hierarchy, hierarchical dimension reduction problem. Firstly it explains the high dimensional data reduction the background, importance and urgency. How to make high-dimensional data dimension reduction algorithm using a more appropriate medical treatment and analysis of massive data, this problem has been a hot topic in today's medical profession, but there are many domestic and foreign experts and scholars have done in this area out a welcome contribution. This paper is to study the issue of huge amounts of data preprocessing, the use of massive data preprocessing is appropriate, is the key to the success of biomarker identification. In this study, also made reference to many domestic and foreign research methods, pericoin the application of cluster analysis combined with genetic algorithms. Lilien principal component analysis and linear discriminant method. Morris the use of wavelet transform and peak detection algorithm. Yu using high-throughput mass spectrometry data algorithm development.
This article focuses on the geometric properties of massive data, given the type of mass data, and dimensionality reduction and data features of the mathematical description of the concept. Secondly, the paper introduces a hierarchical graph-based dimensionality reduction and visual information fusion method to introduce a hierarchical graph models, information visualization, massive data reduction and fusion of visual information visualization information fusion method, and various methods were simple comparison shows the advantages and disadvantages of each method.
第一章主要介绍了海量数据处理和数据融合的背景,强调海量数据处理和数据融合在生物医学、水力部门、金融等方面的重要性。本章基于生物医学领域,阐述了医学数据的庞大和无序,急需要有专业的数据处理技术来处理海量的数据。本文的主要研究内容是提出一种海量数据的处理方法,能够从海量的数据中挖掘出隐含的信息。
第二章主要描述了海量数据的特性和对海量数据预处理的方法。海量数据一般是在超维空间中表示,而超维空间与日常中的低维空间有着明显的区别。低维空间的数据主要分布在空间的中部,而高维空间却恰恰相反,数据主要分布在边缘。所以降维的主要依据就是将数据从超维到低维的转化。数据的类型是多源的,为了将不同数据进行融合和比较,需要将不同的数据进行统一化,归一化。此过程中数据的预处理(如格式转换、归一化、降噪、平滑、降维等)就显得非常重要了。
第三章介绍了基于分层递阶图的降维与可视化信息融合方法,本章重点介绍分层递阶图的模型,海量数据降维和可视化信息融合等,可视化信息融合的方法。
第四章在前几章的理论基础上,提出了一种基于多源海量数据分层递阶图表示模型。并且将此模型运用到脑电数据的可视化处理上,得出仿真结果,说明本文提出的模型的可行性。将此模型的仿真结果与文献中已有模型的仿真结果进行对比,体现本文提出的模型的优越性。
Massive data processing in domestic and international problems and difficulties of a full analysis, this mass of data for the medicine, the main mass of data of high dimensional data hierarchy, hierarchical dimension reduction problem. Firstly it explains the high dimensional data reduction the background, importance and urgency. How to make high-dimensional data dimension reduction algorithm using a more appropriate medical treatment and analysis of massive data, this problem has been a hot topic in today's medical profession, but there are many domestic and foreign experts and scholars have done in this area out a welcome contribution. This paper is to study the issue of huge amounts of data preprocessing, the use of massive data preprocessing is appropriate, is the key to the success of biomarker identification. In this study, also made reference to many domestic and foreign research methods, pericoin the application of cluster analysis combined with genetic algorithms. Lilien principal component analysis and linear discriminant method. Morris the use of wavelet transform and peak detection algorithm. Yu using high-throughput mass spectrometry data algorithm development.
This article focuses on the geometric properties of massive data, given the type of mass data, and dimensionality reduction and data features of the mathematical description of the concept. Secondly, the paper introduces a hierarchical graph-based dimensionality reduction and visual information fusion method to introduce a hierarchical graph models, information visualization, massive data reduction and fusion of visual information visualization information fusion method, and various methods were simple comparison shows the advantages and disadvantages of each method.
引文
1谭郁玲,杨烐林,侯沂等.临床脑电图与脑电地形图学.北京:人民卫生出版社, 1998: 297-300
2瞿亮,凌民,傅昱,蔡力军.基于matlab的控制系统计算机仿真.北京:清华大学出版社,2006:14-46
3舒宁,马洪超,孙和利.模式识别的理论与方法.武昌:武汉大学出版社,2004:4-32
4模式识别—原理方法及应用/(美)马奎斯德萨著:吴逸飞译.北京:清华大学出版社,2002:72-132
5 V. Sehgal,Z.Delproposto,D.Haddar,et al.Susceptibility Weighted Imaging to Visualize Blood Products and Improve Tumor Contrast in the Study of Brain Masses[J].Journal of Magnetic Resonance Imaging,2006,24(1):41-52
6边肇祺,张学工等.模式识别.北京:清华大学出版社,1999:9-117
7 W. OmlinChristian,et al.Inductive bias strength in knowledge-based neural networks. Application to magnetic resonance spectroscopy of breast tissues,2003,28(2):121
8刘同明,夏祖勋,解洪成.数据融合技术及其应用.北京:国防工业出版社,1998:77-141
9 M. Hudelmaier, C. Glaser.Age-related changes in the morphory and deformational behavior of knee joint cartilage. Arthris Rheum, 2001,44(11):2556-2561
10洪文学,李昕,徐永红,王金甲,宋佳霖.基于多元统计图表示原理的信息融合和模式识别技术.北京:国防工业出版社,2008:14-136
11何及.癫痫学.北京:中国中医药出版社,1999:8-16
12 E. M. Haacke,XuYingbiao, Y. N. Chen,et al.Susceptibility Weighted Imaging(SWI)[J]. Magnetic Resonance in Medicine, 2004,52(3): 613-617
13 H.S Liu,T.Zhang and F.S Yang.A Multistage,Multimethod Approach for Automatic Detection and Classification of Epileptiform EEG.IEE Trans Biomed Eng,2004,(3):35-39
14 Mahon, M. Marrita.1H magnetic resonance spectroscopy of invasive cervical cancer: an invivo study with exvivo corroboration [J] . NMR in biomedicine,2004,17(1):1-3
15 Benoit-Cattin H,Collewet G,Belaroussi B.The SMRI project:A versatile and interactive MRI simulator[J].Journal of Magnetic Resonance,2005,173(1):97-115
16 X.L Li,J.Liand X.Yao.Complexity and Synchronization of EEG with Parametric Modelling. IEEE Workshop on Life Science DataMining, Brighton, UK,2004, (3): 34-38
17 M. Bernstein, K. King, X. Zhou. Handbook of MRI Pulse Sequence[M].New York: Elsevier Academic Press, 2004: 31-55
18 L. S. Medina, E. A. guirre,Bernal B. Functional imaging versus Wada test for the evaluation of language lateralization:cost analysis[J].Radiology,2004,230(2):49-54
19 P. REILLYJ.Maximum pulsed electromagnetic field limits based on peripheral nerve stimulation:application to IEEE/ANSIC95.1 electromagnetic field standards[J].IEEE Trans on Biomed Eng,2005,45(1):137-141
20宋毅军,田心.经颅磁刺激对癫痫病灶脑电相关维数的影响.生物物理学报, 2003, 19(4):383-387
21 J.W.Zhang,C.X Zheng and A Xie.Bisperctrum Analysis of Focal Ischemic Cerebral EEG Signal Using Third-ordet Recurion Method,IEEE Trans.Biomed Eng,2000,(47):352-359
22 B.Madore,N.J.Pelc.SMASHand SENSE:Experimental and Numerical Comparisons. Magn Reson Med,2001,45(6):1103-1111
23 X.Li,G.Ouyang. Nonlinear Similarity Analysis for Epileptic Seizures Prediction . Nolin Analysis: Thtory,Methods&Applications,2006,64(8):1666-1687
24 Dandapat S, Ray G C.Spike detection in biome dical signal using midprediction faillter. Medical&Biolocal Engineering&Comprting,1997,(35):354-360
25 Akay,M.Biomedical Signal Processing.Academic Press,1994,(13):99-103
26 P.Paatero and U.Tapper.Positeve Matrix Facorization:A non-gative factor model with optimal utilization of errof estimates data walues.Environ metrics.1994,(15):111-126
27 D.Lee and H.S.Seung.Algorithms for non-negative matrix factorization. Nature, 1999, (Uo1404): 788-791
28 S.Mallat.A theory for multiresolusion signal Dceomposition:The WaveletRepresentation[J]. IEEE Trans on Pattern Analysis and Machine Intelligence ,1989 ,11(7):674-696
29 K.P.Pruessmann,M.Weiger,M.B.Scheidegger,etal.SENSE:Sensitivity Encoding for Fast MRI.Magn Reson Med,1999,42(5):952-962
30 R.Bammer,S.L.Keeling,M.Augustin,etal.Improveddiffusion-weighted single-shotecho planar imaging(EPI) in stroke using sensitivity encoding(SENSE).Magn Reson Med, 2001,46(3):548-554
31 Turk M,Pentland A.Eigenfaces for Recognition [J].Journal of Cognitive Neuroscience (S0898-929X),1991,3(1):71-86
32 C. MATEIB, P. MEER.Estimation of nonlinear Errors in Variables models for omputer vision applications[J].IEEE Trans on Pattern Anal & Machine Intelligence, 2006, 28 (10):1537-1552
33 M. Bydder,D.J.Larkman.Detection and elimination of motion artifacts by regeneration of k-space[J].Magnetic Resonance in Medicine,2002,47(4):677-686
34 R. M. Heidemann,M.A.Griswold, A. Haase.VD-AUTO-SMASH imaging[J]. Magnetic Resonance in Medicine,2001,45(6):1066-1074
35范彦革,刘旭敏.各向异性扩散的研究.计算机工程与应用,2006,29:55-58
36王怀野,张科,李言俊.各向异性扩散滤波的正则化参数选取方法.光子学报,2005, 34(9):1141-1144
37白俊奇,陈钱.基于各向异性扩散的红外图像噪声滤波算法.光学学报,2008, 28(5): 866-869
38 B. Derya, W. P. Ralph. Theory and practice of simultaneous data reconciliation and gross error detection for chemical processes. Computers and Chemical Engineering, 2004,28:381-402
39 Richard Faber,Bo Li,Pu Li.Data reconciliation for real-time optimization of an industrial coke-oven-gas purification process. Simulation Modelling Practice and Theory,2006,14:1121-1134
40 Cheng Hu,Huang Feng.Magnetic resonance imaging image intensity correction with extrapolation and adaptive smoothing[J].Magnetic Resonance in Medicine
41王毅,张良培,李平湘.各向异性扩散平滑滤波的改进算法.中国图象图形学报,2006, 11(2):210-216
42曹芳.基于各点异性理论的基础矩阵鲁棒估计.计算机辅助工程,2008,17(2):57-60
43 Sebastiani F.Machine learning in automated text categorization.ACM Comprting Surveys,2002,34(1):1-37
44舒宁,马洪超.模式识别的理论与方法.武昌:武汉大学出版社,2004:137-146
45谢松云,张振中,杨金孝,张坤.脑电信号的若干处理方法研究与评价.北京:计算机仿真,2007,24(2):326-330
46张美云,张本恕,王凤楼.子波变换在癫痫脑电信号检测和分析中的应用.国际生物医学工程杂志,2006,29(4):255-259
47 S. Weeratunga, C. Kamath.Comparison of PDE-based non-linear anistropic diffusion techniques for image denoisiging.Proc of SPIE,2003,50(14):201-212
48 Ju Shan,Deng Yong,Luo Qingming.Monte Carlo simulation of polarization gating for superficial tissue detection[J].Acta Optica Sinica,2007,27(8):1465-1469
49 A. Gelb.Parameter optimization and reduction of round off error for the Gegenbauer reconstruction method.Journal of scientific Computing,2004,20(3):433-459
50 B. D. Shizgal.Towards the resolution of the Gibbs phenomena.Journal of Computational and Applied Mathematics,2003,161(1):41-65
51 J. H. Jung, B.D.Shizgal.Generalization of the inverse polynomial reconstruction method in the resolution of the Gibbs phenomenon.Journal of Computational and Applied Mathematics,2004,172(1):131-151
2瞿亮,凌民,傅昱,蔡力军.基于matlab的控制系统计算机仿真.北京:清华大学出版社,2006:14-46
3舒宁,马洪超,孙和利.模式识别的理论与方法.武昌:武汉大学出版社,2004:4-32
4模式识别—原理方法及应用/(美)马奎斯德萨著:吴逸飞译.北京:清华大学出版社,2002:72-132
5 V. Sehgal,Z.Delproposto,D.Haddar,et al.Susceptibility Weighted Imaging to Visualize Blood Products and Improve Tumor Contrast in the Study of Brain Masses[J].Journal of Magnetic Resonance Imaging,2006,24(1):41-52
6边肇祺,张学工等.模式识别.北京:清华大学出版社,1999:9-117
7 W. OmlinChristian,et al.Inductive bias strength in knowledge-based neural networks. Application to magnetic resonance spectroscopy of breast tissues,2003,28(2):121
8刘同明,夏祖勋,解洪成.数据融合技术及其应用.北京:国防工业出版社,1998:77-141
9 M. Hudelmaier, C. Glaser.Age-related changes in the morphory and deformational behavior of knee joint cartilage. Arthris Rheum, 2001,44(11):2556-2561
10洪文学,李昕,徐永红,王金甲,宋佳霖.基于多元统计图表示原理的信息融合和模式识别技术.北京:国防工业出版社,2008:14-136
11何及.癫痫学.北京:中国中医药出版社,1999:8-16
12 E. M. Haacke,XuYingbiao, Y. N. Chen,et al.Susceptibility Weighted Imaging(SWI)[J]. Magnetic Resonance in Medicine, 2004,52(3): 613-617
13 H.S Liu,T.Zhang and F.S Yang.A Multistage,Multimethod Approach for Automatic Detection and Classification of Epileptiform EEG.IEE Trans Biomed Eng,2004,(3):35-39
14 Mahon, M. Marrita.1H magnetic resonance spectroscopy of invasive cervical cancer: an invivo study with exvivo corroboration [J] . NMR in biomedicine,2004,17(1):1-3
15 Benoit-Cattin H,Collewet G,Belaroussi B.The SMRI project:A versatile and interactive MRI simulator[J].Journal of Magnetic Resonance,2005,173(1):97-115
16 X.L Li,J.Liand X.Yao.Complexity and Synchronization of EEG with Parametric Modelling. IEEE Workshop on Life Science DataMining, Brighton, UK,2004, (3): 34-38
17 M. Bernstein, K. King, X. Zhou. Handbook of MRI Pulse Sequence[M].New York: Elsevier Academic Press, 2004: 31-55
18 L. S. Medina, E. A. guirre,Bernal B. Functional imaging versus Wada test for the evaluation of language lateralization:cost analysis[J].Radiology,2004,230(2):49-54
19 P. REILLYJ.Maximum pulsed electromagnetic field limits based on peripheral nerve stimulation:application to IEEE/ANSIC95.1 electromagnetic field standards[J].IEEE Trans on Biomed Eng,2005,45(1):137-141
20宋毅军,田心.经颅磁刺激对癫痫病灶脑电相关维数的影响.生物物理学报, 2003, 19(4):383-387
21 J.W.Zhang,C.X Zheng and A Xie.Bisperctrum Analysis of Focal Ischemic Cerebral EEG Signal Using Third-ordet Recurion Method,IEEE Trans.Biomed Eng,2000,(47):352-359
22 B.Madore,N.J.Pelc.SMASHand SENSE:Experimental and Numerical Comparisons. Magn Reson Med,2001,45(6):1103-1111
23 X.Li,G.Ouyang. Nonlinear Similarity Analysis for Epileptic Seizures Prediction . Nolin Analysis: Thtory,Methods&Applications,2006,64(8):1666-1687
24 Dandapat S, Ray G C.Spike detection in biome dical signal using midprediction faillter. Medical&Biolocal Engineering&Comprting,1997,(35):354-360
25 Akay,M.Biomedical Signal Processing.Academic Press,1994,(13):99-103
26 P.Paatero and U.Tapper.Positeve Matrix Facorization:A non-gative factor model with optimal utilization of errof estimates data walues.Environ metrics.1994,(15):111-126
27 D.Lee and H.S.Seung.Algorithms for non-negative matrix factorization. Nature, 1999, (Uo1404): 788-791
28 S.Mallat.A theory for multiresolusion signal Dceomposition:The WaveletRepresentation[J]. IEEE Trans on Pattern Analysis and Machine Intelligence ,1989 ,11(7):674-696
29 K.P.Pruessmann,M.Weiger,M.B.Scheidegger,etal.SENSE:Sensitivity Encoding for Fast MRI.Magn Reson Med,1999,42(5):952-962
30 R.Bammer,S.L.Keeling,M.Augustin,etal.Improveddiffusion-weighted single-shotecho planar imaging(EPI) in stroke using sensitivity encoding(SENSE).Magn Reson Med, 2001,46(3):548-554
31 Turk M,Pentland A.Eigenfaces for Recognition [J].Journal of Cognitive Neuroscience (S0898-929X),1991,3(1):71-86
32 C. MATEIB, P. MEER.Estimation of nonlinear Errors in Variables models for omputer vision applications[J].IEEE Trans on Pattern Anal & Machine Intelligence, 2006, 28 (10):1537-1552
33 M. Bydder,D.J.Larkman.Detection and elimination of motion artifacts by regeneration of k-space[J].Magnetic Resonance in Medicine,2002,47(4):677-686
34 R. M. Heidemann,M.A.Griswold, A. Haase.VD-AUTO-SMASH imaging[J]. Magnetic Resonance in Medicine,2001,45(6):1066-1074
35范彦革,刘旭敏.各向异性扩散的研究.计算机工程与应用,2006,29:55-58
36王怀野,张科,李言俊.各向异性扩散滤波的正则化参数选取方法.光子学报,2005, 34(9):1141-1144
37白俊奇,陈钱.基于各向异性扩散的红外图像噪声滤波算法.光学学报,2008, 28(5): 866-869
38 B. Derya, W. P. Ralph. Theory and practice of simultaneous data reconciliation and gross error detection for chemical processes. Computers and Chemical Engineering, 2004,28:381-402
39 Richard Faber,Bo Li,Pu Li.Data reconciliation for real-time optimization of an industrial coke-oven-gas purification process. Simulation Modelling Practice and Theory,2006,14:1121-1134
40 Cheng Hu,Huang Feng.Magnetic resonance imaging image intensity correction with extrapolation and adaptive smoothing[J].Magnetic Resonance in Medicine
41王毅,张良培,李平湘.各向异性扩散平滑滤波的改进算法.中国图象图形学报,2006, 11(2):210-216
42曹芳.基于各点异性理论的基础矩阵鲁棒估计.计算机辅助工程,2008,17(2):57-60
43 Sebastiani F.Machine learning in automated text categorization.ACM Comprting Surveys,2002,34(1):1-37
44舒宁,马洪超.模式识别的理论与方法.武昌:武汉大学出版社,2004:137-146
45谢松云,张振中,杨金孝,张坤.脑电信号的若干处理方法研究与评价.北京:计算机仿真,2007,24(2):326-330
46张美云,张本恕,王凤楼.子波变换在癫痫脑电信号检测和分析中的应用.国际生物医学工程杂志,2006,29(4):255-259
47 S. Weeratunga, C. Kamath.Comparison of PDE-based non-linear anistropic diffusion techniques for image denoisiging.Proc of SPIE,2003,50(14):201-212
48 Ju Shan,Deng Yong,Luo Qingming.Monte Carlo simulation of polarization gating for superficial tissue detection[J].Acta Optica Sinica,2007,27(8):1465-1469
49 A. Gelb.Parameter optimization and reduction of round off error for the Gegenbauer reconstruction method.Journal of scientific Computing,2004,20(3):433-459
50 B. D. Shizgal.Towards the resolution of the Gibbs phenomena.Journal of Computational and Applied Mathematics,2003,161(1):41-65
51 J. H. Jung, B.D.Shizgal.Generalization of the inverse polynomial reconstruction method in the resolution of the Gibbs phenomenon.Journal of Computational and Applied Mathematics,2004,172(1):131-151