基于独立分量分析特征提取方法的研究及其应用
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摘要
独立分量分析(ICA)是一种非常有效的盲信号处理技术,其基本原理是通过分析多维观测数据间的高阶统计相关性,找出相互独立的隐含信息成分,完成分量间高阶冗余的去除及独立信源的提取。这一特点使得ICA方法在无线通信、语音处理、生物医学、图像处理等领域中有着广泛的应用前景,并已成为国际上信号处理和模式识别等领域的研究热点。本文将对ICA特征提取方法进行研究。
支持向量机(SVM)是在统计学习理论的VC维理论和结构风险最小化原则的基础上提出的一种新的机器学习方法,它追求的是在有限样本情况下的最优解而不仅仅是样本数趋于无穷大时的最优解,比以经验风险最小化为基础的神经网络学习算法具有更强的理论依据和更好的泛化性能。因此SVM被广泛地应用于模式识别领域。
由于ICA尚处于发展阶段,其理论和应用的研究都有待进一步深化。本文在介绍独立分量分析方法的基本原理及算法的基础上,针对快速独立分量分析(FastICA)特征提取方法时间过长提出了ICA改进算法;并研究了将ICA作为特征提取手段与SVM结合应用于混合气体定量分析与心电图分类识别。主要在以下方面进行了研究与探讨:
(1)介绍了独立分量分析基础理论知识,阐述了基于信息论的实现算法,并着重分析和研究了两种常用的ICA算法FastICA和基于信息最大化的独立分量分析(InfoMax ICA)的具体实现过程。
(2)核独立分量分析(KICA)是最近发展的一种非线性ICA方法,本文提出了一种在混合气体定量分析中应用核独立成分分析(KICA)与最小二乘支持向量回归机(LSSVR)相结合进行模式识别的方法。该方法运用KICA方法对气体传感器所测数据进行特征提取,以减少数据之间的相关性,并通过LSSVR实现混合气体的定量识别。对从丁烷和乙醇的混合气体中所测得的原始数据进行实验,实验结果表明了KICA方法的有效性。
(3)提出利用埃特金(aitken)加速法对FastICA的核心迭代过程进行改造,得到新的改进快速ICA算法(I-FastICA),减少了迭代次数,提高了收敛速度。将I-FastICA方法与SVM相结合用于心电图(ECG)分类识别,其中,运用I-FastICA算法提取ECG数据的特征向量,并通过SVM实现ECG信号的分类。对取自MIT/BH数据库的7种不同心脏状况的ECG数据进行实验。实验证明,I-FastICA方法在保证与传统FastICA方法相当分离精度的同时,计算时间得到缩短。
Independent Component Analysis (ICA) is a kind of powerful method for Blind Signal Processing (BSP). The principle of ICA algorithm is to find the potential mutual independent components, to remove higher-order redundance between components and to extract the independent original signals source by analyzing the high-order statistical correlation of the multidimensional data. This property leads to a promising prospect of ICA in applied fields such as telecommunications, audio signal separation, biomedical signal processing, and image processing. And ICA becomes one of the most exciting new topics both in the fields of signal processing and pattern recognition. The main work of this thesis is to discuss the ICA as a method of feature extraction.
Support Vector Machine (SVM) is a new kind of machine learning algorithm proposed recently which is based on VC dimension theory and structural risk minimization of statistical learning theory. SVM can obtain the optimum result from the gained information which is not the optimum result only when the samples are infinite. SVM has much stronger theory foundation and better generalization than neural network which is based on empirical risk minimization. So SVM is popular in recognition field.
ICA is still staying at the developing stage, and the investigation of its theory and application should be enhanced and improved further. The basic principles of ICA and some algorithms are introduced in this thesis. Aiming at the problem that Fast ICA feature extraction has the shortcoming of long computing time, a modified ICA algorithm is suggested. And we proposed a scheme to integrate ICA feature extraction and SVM for gas mixture quantitative analysis and electrocardiogram (ECG) diagnosis. The main works in this thesis are as follows:
(1) The basic principles of ICA and some algorithms based on information theory are introduced. Two kinds of effectual algorithms in common use are discussed in detail, FastICA and InfoMax ICA.
(2) Kernel Independent Component Analysis (KICA) is a new developed nonlinear ICA algorithm. We proposed a pattern recognition method for gas mixture quantitative analysis by combined use of KICA and least squares support vector regression (LSSVR) in this thesis. In the proposed method, the KICA algorithm based on an entire function space of nonlinear subspace is firstly used for preprocessing gas sensor data, which can reduce the data correlation. And then a LSSVR carries out the gas mixture recognition, The measuring data was obtained from a gas mixture of butane and ethanol for experiments. The results indicate that the KICA method is efficient.
(3) Modifying the kernel iterative procedure of FastICA with accelerating aitken method, an improved FastICA (I-FastICA) algorithm is given. The I-FastICA algorithm can cut down iteration times. Also, the convergence process of the algorithm can be accelerated. We proposed a scheme to integrate improved fast independent component analysis I-FastICA and SVM for ECG diagnosis. In the proposed method, the I-FastICA algorithm is firstly used for feature extraction of ECG data, and then a SVM carries out the ECG signal classification. The ECG samples attributing to seven different beats types were sampled from the MIT-BIH arrhythmia database for experiments. The results show that the I-FastICA algorithm reduces computation time with the correspondent separation performance.
支持向量机(SVM)是在统计学习理论的VC维理论和结构风险最小化原则的基础上提出的一种新的机器学习方法,它追求的是在有限样本情况下的最优解而不仅仅是样本数趋于无穷大时的最优解,比以经验风险最小化为基础的神经网络学习算法具有更强的理论依据和更好的泛化性能。因此SVM被广泛地应用于模式识别领域。
由于ICA尚处于发展阶段,其理论和应用的研究都有待进一步深化。本文在介绍独立分量分析方法的基本原理及算法的基础上,针对快速独立分量分析(FastICA)特征提取方法时间过长提出了ICA改进算法;并研究了将ICA作为特征提取手段与SVM结合应用于混合气体定量分析与心电图分类识别。主要在以下方面进行了研究与探讨:
(1)介绍了独立分量分析基础理论知识,阐述了基于信息论的实现算法,并着重分析和研究了两种常用的ICA算法FastICA和基于信息最大化的独立分量分析(InfoMax ICA)的具体实现过程。
(2)核独立分量分析(KICA)是最近发展的一种非线性ICA方法,本文提出了一种在混合气体定量分析中应用核独立成分分析(KICA)与最小二乘支持向量回归机(LSSVR)相结合进行模式识别的方法。该方法运用KICA方法对气体传感器所测数据进行特征提取,以减少数据之间的相关性,并通过LSSVR实现混合气体的定量识别。对从丁烷和乙醇的混合气体中所测得的原始数据进行实验,实验结果表明了KICA方法的有效性。
(3)提出利用埃特金(aitken)加速法对FastICA的核心迭代过程进行改造,得到新的改进快速ICA算法(I-FastICA),减少了迭代次数,提高了收敛速度。将I-FastICA方法与SVM相结合用于心电图(ECG)分类识别,其中,运用I-FastICA算法提取ECG数据的特征向量,并通过SVM实现ECG信号的分类。对取自MIT/BH数据库的7种不同心脏状况的ECG数据进行实验。实验证明,I-FastICA方法在保证与传统FastICA方法相当分离精度的同时,计算时间得到缩短。
Independent Component Analysis (ICA) is a kind of powerful method for Blind Signal Processing (BSP). The principle of ICA algorithm is to find the potential mutual independent components, to remove higher-order redundance between components and to extract the independent original signals source by analyzing the high-order statistical correlation of the multidimensional data. This property leads to a promising prospect of ICA in applied fields such as telecommunications, audio signal separation, biomedical signal processing, and image processing. And ICA becomes one of the most exciting new topics both in the fields of signal processing and pattern recognition. The main work of this thesis is to discuss the ICA as a method of feature extraction.
Support Vector Machine (SVM) is a new kind of machine learning algorithm proposed recently which is based on VC dimension theory and structural risk minimization of statistical learning theory. SVM can obtain the optimum result from the gained information which is not the optimum result only when the samples are infinite. SVM has much stronger theory foundation and better generalization than neural network which is based on empirical risk minimization. So SVM is popular in recognition field.
ICA is still staying at the developing stage, and the investigation of its theory and application should be enhanced and improved further. The basic principles of ICA and some algorithms are introduced in this thesis. Aiming at the problem that Fast ICA feature extraction has the shortcoming of long computing time, a modified ICA algorithm is suggested. And we proposed a scheme to integrate ICA feature extraction and SVM for gas mixture quantitative analysis and electrocardiogram (ECG) diagnosis. The main works in this thesis are as follows:
(1) The basic principles of ICA and some algorithms based on information theory are introduced. Two kinds of effectual algorithms in common use are discussed in detail, FastICA and InfoMax ICA.
(2) Kernel Independent Component Analysis (KICA) is a new developed nonlinear ICA algorithm. We proposed a pattern recognition method for gas mixture quantitative analysis by combined use of KICA and least squares support vector regression (LSSVR) in this thesis. In the proposed method, the KICA algorithm based on an entire function space of nonlinear subspace is firstly used for preprocessing gas sensor data, which can reduce the data correlation. And then a LSSVR carries out the gas mixture recognition, The measuring data was obtained from a gas mixture of butane and ethanol for experiments. The results indicate that the KICA method is efficient.
(3) Modifying the kernel iterative procedure of FastICA with accelerating aitken method, an improved FastICA (I-FastICA) algorithm is given. The I-FastICA algorithm can cut down iteration times. Also, the convergence process of the algorithm can be accelerated. We proposed a scheme to integrate improved fast independent component analysis I-FastICA and SVM for ECG diagnosis. In the proposed method, the I-FastICA algorithm is firstly used for feature extraction of ECG data, and then a SVM carries out the ECG signal classification. The ECG samples attributing to seven different beats types were sampled from the MIT-BIH arrhythmia database for experiments. The results show that the I-FastICA algorithm reduces computation time with the correspondent separation performance.
引文
[ 1 ] Jolliffe I T. Principal component analysis [M]. New York: Springer, 1986.
[ 2 ] Fukunaga K. Intrduction to statistical pattern classification [M]. California: Academic Press, 1990.
[ 3 ] Ans B, Herault J, Jutten C. Adaptive neural architectures: detection of primitives [C]. In Proc. Of COGNITIVA'05, 1985: 593-597.
[ 4 ] Jutten C, Herault J. Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture [J]. Signal Processing, 1991, 24(1): 1-10.
[ 5 ] Comon P. Independent Component Analysis, a new concept? [J]. Signal Precessing, 1994, 36(3): 191-235.
[ 6 ] Bell A J, Sejnowski T J. An information- maximization approach to blind separation and blind deconvolution [J]. Neural computation, 1995, 7(6): 1129-1159.
[ 7 ] Bell A J, Sejnowski T J. A non-linear information maximization algorithm that performs blind separation. In Advances in Neural Information Processing Systems 7 [M], The MIT Press, 1995.
[ 8 ] Cardoso J F. Laheld B H. Equivariant adaptive source separation [J]. Signal Processing, 1996, 44(12): 3017-3030.
[9] Amari S I. Natural gradient works efficiently in learning [J]. Neural computation, 1998, 10(2): 251-276.
[10] Cichocki A, Unbehauen R. Robust neural networks with on-line learning for blind identification and blind separation of sources [J]. Circuit and Systems, 1996,43(11): 894-906.
[11] Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis [J]. Neural Networks, 1999, 10(3): 626-634.
[12] Deniz O, Castrillon M, Hernandez. Face recognition using independent component analysis and support vector machine [J]. Pattern Recognition Letters, 2003, 24(13): 2153-2157.
[13] Chuang C F, Shih F Y. Recognizing facial action units using independent component analysis and support vector [J]. Pattern Recognition, 2006, 39(9): 1795-1798.
[14] Lucia D, Marzia. A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis [J]. Medical and Biological Engineering and Computing, 2008,46(3): 263-272.
[15] Owis M I, Youssef A M, Kadah Y M. Characterization of ECG signals based on blind source separation [J], Medical and Biological Engineering and computing, 2002, 40(5): 557-564.
[16] Yu S N, Chou K T. Integration of independent component analysis and neural networks for ECG beat classification [J]. Expert Systems with Applications, 2008,34(4): 2841-2846.
[17] Yadava R D S, Chaudhary R. Solvation, transduction and independent component analysis for pattern recognition in SAW electronic nose [J]. Sensors and Actuators B, 2006, 113(1): 1 -21.
[18] Kermit M, Tomic O. Independent component analysis applied on gas sensor array measurement data [J]. Sensors Journal, 2003, 3(2): 218-228.
[19] Kwon O W, Lee T W. Phoneme recognition using ICA-based feature extraction and transformation [J]. Signal Processing, 2004, 84(6): 1005-1019.
[20] Widodo A, Yang B S, Han T. Combination of independent component analysis and support vector machine for intelligent faults diagnosis of induction motors [J]. Experts Systems with Applications, 2007,32(2): 299-312.
[21] Vapnik V.. The nature of statistical learning theory [M]. New York: Springer- Verlag, 1995.
[22] Bach F R, Jordan M I. Kernel Independent Component Analysis [J]. Journal of Machine Learning Research, 2002,3: 1-48.
[23] Suykens J A K, Vandewalle J. Least squares support vector machine classifiers [J]. Neural Process Letters, 1999, 9(3): 293-300.
[24] Tong L, Liu R, Soon V, etal. Indeterminacy and Identifiability of Blind Identification [J]. Circuits System, 1991, 38(5): 499-509.
[25] Linsker R. Self- organization in a perceptual network [J]. IEEE Computer, 1988,21: 105-117.
[26] Lee T W, Girolami M, Sejnowski T J. Independent Component Analysis using extended infomax algorithm for mixed subgaussian and supergaussian sources [J]. Neural Computation, 1999, 11(2): 417-441.
[27] Gardner J W, Bartlett PN. A Brief History of Electronic Noses [J]. Sensors and Actuators B, 1994, 18(1-3): 211-220.
[28] Gardner J W, Bartlett P N. Electronic Noses: Principles and Applications [M]. Oxford University Press, 1999.
[29] 曲建岭,孙福娟．基于人工神经网络的混合气体定量检测方法[J]．测试技术学报,2003, 17(4)：309-312．
[30] Moore S W, Gardner J W, Hines E L, etal. A modified multiplayer perceptron model for gas mixture analysis [J]. Sensors and Actuators B, 1993, 16(1-3): 344-348.
[31] Eklov T, Lundstrom I. Gas mixture analysis using a distributed chemical sensor system [J]. Sensors and Actuators B, 1999, 57(1-3): 274-282.
[32] Negri R M, Reich S. Identification of pollutant gases and its concentrations with a multisensor array [J]. Sensors and Actuators B, 2001,75(3): 172-178.
[33] Gulbag A, Temurtas F. A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems [J]. Sensors and Actuators B. 2006, 115(1): 252-262.
[34] Vergara A, Llobet E, Brezmes J. Quantitative gas mixture analysis using temperature-modulated micro-hotplate gas sensors: Selection and validation of the optimal modulating frequencies [J]. Sensors and Actuators B,2007,123(2):1002-1016.
[35]曲建岭.人工嗅觉系统中的信息处理技术[D].西北工业大学,2000.
[36]王玮.基于气体传感器阵列的混合气体检测系统[D].西北工业大学,2002.
[37]于鹏.人工嗅觉系统检测室内污染混合气体的研究[D].浙江大学,2003.
[38]宋委远.基于传感器阵列的混合气体测试系统研究[D].华中科技大学,2006.
[39]王岩,陈向东,赵静.基于FastICA和神经网络的电子鼻模式识别[J].传感技术学报,2007,20(1):38-41.
[40]王鹤,周东祥,宋委远,等.基于遗传神经网络的混合气体识别研究[J].华中科技大学学报.2007,35(9):118-120.
[41]Kermit M,Tomic O.Independent component analysis applied on gas sensor array measurement data[J].Sensors Journal,2003,3(2):218-228.
[42]Scholkopf B,Smola A J.Learning with Kernels[M].The MIT Press,2002.
[43]徐保港.基于气体传感器阵列的嗅觉机器人的研究[D].哈尔滨工业大学,2006.
[44]Demuth H,Beale M,Hagan M.Neural Network Toolbox 5 User's Guide.The MathWorks Inc.,2006.
[45]Gulera I,Ubeyh E D.ECG beat classifier designed by combined neural network model[J].Pattern Recognition,2005,38(2):199-208.
[46]Gulera I,Ubeyh E D.A modified mixture of experts network structure for ECG beats classification with diverse features[J].Engineering Applications of Artificial Intelligence,2005,18:845-848.
[47]Osowski S,Linh TH.ECG beat recognition using fuzzy hybrid neural network[J].Biomedical Engineering,2001,48(11):1265-1271.
[48]Osowski S,Hoai LT,Markiewicz T.Support vector machine-based expert system for reliable heartbeat recognition[J].Biomedical Engineering,2004,51(4):582-589.
[49]Owis M I,Youssef A M,Kadah Y M.Characterization of ECG signals based on blind source separation[J],Medical and Biological Engineering and computing,2002,40(5):557-564.
[50]曹玉珍,范增飞.基于最大散度的特征搜索算法用于心搏分类的研究[J].生物医学工程学杂志,2008,25(1):53-56.
[51]刘世雄.基于模糊聚类算法对心电数据典型特征分类研究[D].浙江大学,2006.
[52]骆德汉,许广桂,邹宇华.基于多阶人工神经网络的ECG信号诊断模型研究[J].仪器仪表学报,2008,29(1):27-32.
[53]季虎.心电信号自动分析关键技术研究[D].国防科技大学,2006.
[54]周珂,彭宏,胡劲松,等.粗糙集在心电图分类诊断中的应用[J].计算机工程与应用,2006,42(13):206-208.
[55]曾生根,朱宁波,包晔.一种改进的快速独立分量分析算法及其在图像分离中的应用[J].中国图像图形学报,2003,8(10):1159-1165.
[ 2 ] Fukunaga K. Intrduction to statistical pattern classification [M]. California: Academic Press, 1990.
[ 3 ] Ans B, Herault J, Jutten C. Adaptive neural architectures: detection of primitives [C]. In Proc. Of COGNITIVA'05, 1985: 593-597.
[ 4 ] Jutten C, Herault J. Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture [J]. Signal Processing, 1991, 24(1): 1-10.
[ 5 ] Comon P. Independent Component Analysis, a new concept? [J]. Signal Precessing, 1994, 36(3): 191-235.
[ 6 ] Bell A J, Sejnowski T J. An information- maximization approach to blind separation and blind deconvolution [J]. Neural computation, 1995, 7(6): 1129-1159.
[ 7 ] Bell A J, Sejnowski T J. A non-linear information maximization algorithm that performs blind separation. In Advances in Neural Information Processing Systems 7 [M], The MIT Press, 1995.
[ 8 ] Cardoso J F. Laheld B H. Equivariant adaptive source separation [J]. Signal Processing, 1996, 44(12): 3017-3030.
[9] Amari S I. Natural gradient works efficiently in learning [J]. Neural computation, 1998, 10(2): 251-276.
[10] Cichocki A, Unbehauen R. Robust neural networks with on-line learning for blind identification and blind separation of sources [J]. Circuit and Systems, 1996,43(11): 894-906.
[11] Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis [J]. Neural Networks, 1999, 10(3): 626-634.
[12] Deniz O, Castrillon M, Hernandez. Face recognition using independent component analysis and support vector machine [J]. Pattern Recognition Letters, 2003, 24(13): 2153-2157.
[13] Chuang C F, Shih F Y. Recognizing facial action units using independent component analysis and support vector [J]. Pattern Recognition, 2006, 39(9): 1795-1798.
[14] Lucia D, Marzia. A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis [J]. Medical and Biological Engineering and Computing, 2008,46(3): 263-272.
[15] Owis M I, Youssef A M, Kadah Y M. Characterization of ECG signals based on blind source separation [J], Medical and Biological Engineering and computing, 2002, 40(5): 557-564.
[16] Yu S N, Chou K T. Integration of independent component analysis and neural networks for ECG beat classification [J]. Expert Systems with Applications, 2008,34(4): 2841-2846.
[17] Yadava R D S, Chaudhary R. Solvation, transduction and independent component analysis for pattern recognition in SAW electronic nose [J]. Sensors and Actuators B, 2006, 113(1): 1 -21.
[18] Kermit M, Tomic O. Independent component analysis applied on gas sensor array measurement data [J]. Sensors Journal, 2003, 3(2): 218-228.
[19] Kwon O W, Lee T W. Phoneme recognition using ICA-based feature extraction and transformation [J]. Signal Processing, 2004, 84(6): 1005-1019.
[20] Widodo A, Yang B S, Han T. Combination of independent component analysis and support vector machine for intelligent faults diagnosis of induction motors [J]. Experts Systems with Applications, 2007,32(2): 299-312.
[21] Vapnik V.. The nature of statistical learning theory [M]. New York: Springer- Verlag, 1995.
[22] Bach F R, Jordan M I. Kernel Independent Component Analysis [J]. Journal of Machine Learning Research, 2002,3: 1-48.
[23] Suykens J A K, Vandewalle J. Least squares support vector machine classifiers [J]. Neural Process Letters, 1999, 9(3): 293-300.
[24] Tong L, Liu R, Soon V, etal. Indeterminacy and Identifiability of Blind Identification [J]. Circuits System, 1991, 38(5): 499-509.
[25] Linsker R. Self- organization in a perceptual network [J]. IEEE Computer, 1988,21: 105-117.
[26] Lee T W, Girolami M, Sejnowski T J. Independent Component Analysis using extended infomax algorithm for mixed subgaussian and supergaussian sources [J]. Neural Computation, 1999, 11(2): 417-441.
[27] Gardner J W, Bartlett PN. A Brief History of Electronic Noses [J]. Sensors and Actuators B, 1994, 18(1-3): 211-220.
[28] Gardner J W, Bartlett P N. Electronic Noses: Principles and Applications [M]. Oxford University Press, 1999.
[29] 曲建岭,孙福娟．基于人工神经网络的混合气体定量检测方法[J]．测试技术学报,2003, 17(4)：309-312．
[30] Moore S W, Gardner J W, Hines E L, etal. A modified multiplayer perceptron model for gas mixture analysis [J]. Sensors and Actuators B, 1993, 16(1-3): 344-348.
[31] Eklov T, Lundstrom I. Gas mixture analysis using a distributed chemical sensor system [J]. Sensors and Actuators B, 1999, 57(1-3): 274-282.
[32] Negri R M, Reich S. Identification of pollutant gases and its concentrations with a multisensor array [J]. Sensors and Actuators B, 2001,75(3): 172-178.
[33] Gulbag A, Temurtas F. A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems [J]. Sensors and Actuators B. 2006, 115(1): 252-262.
[34] Vergara A, Llobet E, Brezmes J. Quantitative gas mixture analysis using temperature-modulated micro-hotplate gas sensors: Selection and validation of the optimal modulating frequencies [J]. Sensors and Actuators B,2007,123(2):1002-1016.
[35]曲建岭.人工嗅觉系统中的信息处理技术[D].西北工业大学,2000.
[36]王玮.基于气体传感器阵列的混合气体检测系统[D].西北工业大学,2002.
[37]于鹏.人工嗅觉系统检测室内污染混合气体的研究[D].浙江大学,2003.
[38]宋委远.基于传感器阵列的混合气体测试系统研究[D].华中科技大学,2006.
[39]王岩,陈向东,赵静.基于FastICA和神经网络的电子鼻模式识别[J].传感技术学报,2007,20(1):38-41.
[40]王鹤,周东祥,宋委远,等.基于遗传神经网络的混合气体识别研究[J].华中科技大学学报.2007,35(9):118-120.
[41]Kermit M,Tomic O.Independent component analysis applied on gas sensor array measurement data[J].Sensors Journal,2003,3(2):218-228.
[42]Scholkopf B,Smola A J.Learning with Kernels[M].The MIT Press,2002.
[43]徐保港.基于气体传感器阵列的嗅觉机器人的研究[D].哈尔滨工业大学,2006.
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