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1.
A switchable scheme is proposed to discriminate different types of electrocardiogram (ECG) beats based on independent component analysis (ICA). The RR-interval serves as an indicator for the scheme to select between the longer (1.0 s) and the shorter (0.556 s) data samples for the following processing. Six ECG beat types, including 13900 samples extracted from 25 records in the MIT-BIH database, are employed in this study. Three conventional statistical classifiers are employed to testify the discrimination power of this method. The result shows a promising accuracy of over 99%, with equally well recognition rates throughout all types of ECG beats. Only 27 ICA features are needed to attain this high accuracy, which is substantially smaller in quantity than that in the other methods. The results prove the capability of the proposed scheme in characterizing heart diseases based on ECG signals.  相似文献   

2.
人脸检测中基于自适应ICA的特征提取算法   总被引:1,自引:0,他引:1  
赵伟达  张丽清 《计算机仿真》2007,24(10):204-208
如何从图片中提取出有效特征来区分人脸与非人脸一直是一个难题.文中提出了利用自适应独立成分分析(Self-Adaptive ICA)算法对图像结构信息非常敏感的特点,有效地从大量正面人脸图片中分离出人脸的局部特征,从而利用这些局部特征基底有效地表示人脸图片.自适应ICA算法的优点是能自适应的拟合图像数据的统计性质,而不用预先设定.通过比较待检测的人脸图片与非人脸图片在这组特征基底上的投影系数,可以较好的区分二者.实验结果也表明这种特征提取方法可以找到一组很好的人脸特征基底.使用这种方法构造的弱分类器的分类准确率在相同的误检率下比Boosted Cascaded方法中的弱分类器高1% ~ 1.5%.  相似文献   

3.
研究地下异常体的有用信息,由于需要野外采集,所接收的瞬变电磁信号会叠加各种电磁干扰和噪声,而传统的降噪方法不能较好地对瞬变电磁接收的二次场衰减信号降噪,严重影响了利用信号对地下异常体特征的数据成图和地质状况解释.针对上述问题,提出了改进的基于独立成分分析的瞬变电磁接收信号降噪处理算法.由接收的瞬变电磁信号构成多维观测向量,利用独立成分分析方法从观测向量中分离出信号空间基向量,然后采用峰度判别准则对信号空间和噪声空间进行分离,保留二次场信号基向量,用二次场信号基向量张成信号子空间,实现降低噪声的目的.降噪后的瞬变电磁信号可以更好地反演出地下相关信息.通过对实测数据的验证,结果表明提出的算法应用于瞬变电磁法信号降噪是可行的,能有效地降低噪声并分离出二次场信号的有用信息,对提高瞬变电磁数据成图和地质状况解释质量具有较好的作用.  相似文献   

4.
Handling of incomplete data sets using ICA and SOM in data mining   总被引:1,自引:0,他引:1  
Based on independent component analysis (ICA) and self-organizing maps (SOM), this paper proposes an ISOM-DH model for the incomplete data’s handling in data mining. Under these circumstances the data remain dependent and non-Gaussian, this model can make full use of the information of the given data to estimate the missing data and can visualize the handled high-dimensional data. Compared with mixture of principal component analyzers (MPCA), mean method and standard SOM-based fuzzy map model, ISOM-DH model can be applied to more cases, thus performing its superiority. Meanwhile, the correctness and reasonableness of ISOM-DH model is also validated by the experiment carried out in this paper.  相似文献   

5.
Recently, in a task of face recognition, some researchers presented that independent component analysis (ICA) Architecture I involves a vertically centered principal component analysis (PCA) process (PCA I) and ICA Architecture II involves a whitened horizontally centered PCA process (PCA II). They also concluded that the performance of ICA strongly depends on its involved PCA process. This means that the computationally expensive ICA projection is unnecessary for further process and involved PCA process of ICA, whether PCA I or II, can be used directly for face recognition. But these approaches only consider the global information of face images. Some local information may be ignored. Therefore, in this paper, the sub-pattern technique was combined with PCA I and PCA II, respectively, for face recognition. In other words, two new different sub-pattern based whitened PCA approaches (which are called Sp-PCA I and Sp-PCA II, respectively) were performed and compared with PCA I, PCA II, PCA, and sub-pattern based PCA (SpPCA). Then, we find that sub-pattern technique is useful to PCA I but not to PCA II and PCA. Simultaneously, we also discussed what causes this result in this paper. At last, by simultaneously considering global and local information of face images, we developed a novel hybrid approach which combines PCA II and Sp-PCA I for face recognition. The experimental results reveal that the proposed novel hybrid approach has better recognition performance than that obtained using other traditional methods.  相似文献   

6.
A novel ensemble of classifiers for microarray data classification   总被引:1,自引:0,他引:1  
Yuehui  Yaou   《Applied Soft Computing》2008,8(4):1664-1669
Micorarray data are often extremely asymmetric in dimensionality, such as thousands or even tens of thousands of genes and a few hundreds of samples. Such extreme asymmetry between the dimensionality of genes and samples presents several challenges to conventional clustering and classification methods. In this paper, a novel ensemble method is proposed. Firstly, in order to extract useful features and reduce dimensionality, different feature selection methods such as correlation analysis, Fisher-ratio is used to form different feature subsets. Then a pool of candidate base classifiers is generated to learn the subsets which are re-sampling from the different feature subsets with PSO (Particle Swarm Optimization) algorithm. At last, appropriate classifiers are selected to construct the classification committee using EDAs (Estimation of Distribution Algorithms). Experiments show that the proposed method produces the best recognition rates on four benchmark databases.  相似文献   

7.
基于二维图像矩阵的ICA人脸识别   总被引:2,自引:0,他引:2  
为了解决传统独立分量分析(ICA)在人脸识别过程中存在的高维小样本问题,同时为了提高识别效率,提出了一种基于二维图像矩阵的独立分量分析(ICA)特征提取方法.该方法将人脸图像矩阵作为训练样本,首先利用主分量分析(PCA)对训练样本进行去二阶相关和降维处理,然后对处理后的样本进行ICA特征提取,由于训练样本维数很小,因此它降低了传统ICA方法中高维小样本问题产生的识别错误率,同时减少了识别时间.在Yale人脸库和ORL人脸库上验证了该算法的有效性.  相似文献   

8.
在独立分量分析 (Independent component analysis, ICA) 中, 寻找去除高阶相关的正交矩阵成为问题关键, 而正交矩阵具有特殊的空间结构, 组成它的每个列向量可视作 RN 中单位超球表面上一点, 当这些点彼此垂直时, 整体就组成一个正交矩阵. 自然地, 这些点可以用其球坐标来参数化. 本文通过观察正交矩阵的几何结构, 找到了任意维数的随机正交矩阵的参数表示方法, 且论证了这种表示的完备性; 同时, 对随机正交矩阵参数表示的随机性做了定量分析; 然后, 利用遗传算法对参数化正交矩阵中的参数进行搜索, 得到了分离结果. 本文称这种算法为 OICA 算法, 并给出了该算法的仿真实验.  相似文献   

9.
多目标遗传算法(MOGA)是求解多目标优化问题的有效工具,因而在求解实际问题中得到越来越广泛的应用.PCA是一种基于二阶统计的最小均方误差意义上的最优维数压缩技术,PCA方法所抽取特征的各分量之间是统计不相关的.在人脸识别的实际应用中,将多目标遗传算法引入到PCA所生成的特征空间的优化中,提出基于双重特征空间的人脸识别算法.通过对剑桥ORL库实验表明,该方法与传统的PCA相比,识别率得到明显提高.  相似文献   

10.
Independent component analysis (ICA) is a newly developed promising technique in signal processing applications. The effective separation and discrimination of functional Magnetic Resonance Imaging (fMRI) signals is an area of active research and widespread interest. Therefore, the development of an ICA based fMRI data processing method is of obvious value both theoretically and in potential applications. In this paper, analyzed firstly is the drawback of the extant popular ICA-fMRI method where the adopted signal model assumes the independence of spatial distributions of the signals and noise. Then presented is a new fMRI signal model, which assumes the independence of temporal courses of signal and noise in a tiny spatial domain. Consequently we get a novel fMRI data processing method: Neighborhood independent component correlation algorithm. The effectiveness is elucidated through theoretical analysis and simulation tests, and finally a real fMRI data test is presented.  相似文献   

11.
The study of the sensitivity and the specificity of a classification test constitute a powerful kind of analysis since it provides specialists with very detailed information useful for cancer diagnosis. In this work, we propose the use of a multiobjective genetic algorithm for gene selection of Microarray datasets. This algorithm performs gene selection from the point of view of the sensitivity and the specificity, both used as quality indicators of the classification test applied to the previously selected genes. In this algorithm, the classification task is accomplished by Support Vector Machines; in addition a 10-Fold Cross-Validation is applied to the resulting subsets. The emerging behavior of all these techniques used together is noticeable, since this approach is able to offer, in an original and easy way, a wide range of accurate solutions to professionals in this area. The effectiveness of this approach is proved on public cancer datasets by working out new and promising results. A comparative analysis of our approach using two and three objectives, and with other existing algorithms, suggest that our proposal is highly appropriate for solving this problem.  相似文献   

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