【作者】 谢亮；

【导师】 汪晓东；

【作者基本信息】 浙江师范大学 ， 计算机软件与理论， 2009， 硕士

【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。更多还原

【Abstract】 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.更多还原