基于多层自组织映射和主成分分析的入侵检测方法*

首先改进了自组织映射学习和分类算法,通过引入自定义变量匹配度、约简率和约简样本量化误差,提出了一种新的基于多层自组织映射和主成分分析入侵检测模型与算法。模型运用主成分分析算法对输入样本进行特征约简,运用分层思想对分类精度低的聚类进行逐层细分,解决了单层自组织映射分类不精确的问题。实验结果表明该模型用于入侵检测的效果良好,能准确区分攻击与否且能进一步指出攻击的具体类型。     

基于2D-KPCA的拉普拉斯特征映射人脸识别*

针对拉普拉斯特征映射(LE)只能保持局部近邻信息,对新测试点无法描述的不足,提出一种基于二维核主成分分析的拉普拉斯特征映射算法(2D-KPCA LE)。与核二维主成分分析算法(K2DPCA)不同,该算法首先对训练样本空间进行二维主成分分析（2DPCA）,在保留样本空间结构信息的同时通过去相关性得到低秩的投影特征矩阵;然后用核主成分分析法(KPCA)提取全局非线性特征;由于其核函数需要大量存储空间,再用拉普拉斯特征映射(LE)进行降维。在ORL和FERET人脸数据库中的仿真实验结果表明,基于2D-KPCA的拉普拉斯特征映射算法不但可以有效处理复杂的非线性特征,又可以降低算法复杂度,提高流形学习的识别率。     

基于多维自组织特征映射的聚类算法研究

作为神经网络的一种方法,自组织特征映射在数据挖掘、模式分类和机器学习中得到了广泛应用.本文详细讨论了自组织特征映射的聚类算法的工作原理和具体实现算法.通过系统仿真实验分析,SOFMF算法很好地克服了许多聚类算法存在的问题,在时间复杂度上具有良好的性能.     

自组织映射(SOM)聚类算法的研究

通过自组织映射神经网络实现的聚类算法能将任意维数的输入信号模式转变为一维或二维的离散映射,以拓扑有序的方式自适应实现这个变换.介绍自组织映射聚类算法的原理,通过实验进行仿真,结果表明自组织映射聚类算法是可行有效的.     

利用自组织特征映射神经网络进行可视化聚类

自组织特征映射作为一种神经网络方法，在数据挖掘、机器学习和模式分类中得到了广泛的应用。它将高维输人空间的数据映射到一个低维、规则的栅格上，从而可以利用可视化技术探测数据的固有特性。该文说明了自组织特征映射神经网络的工作原理和具体实现算法，同时利用一个算例展示了利用自组织特征映射进行聚类时的可视化特性，包括聚类过程的可视化和聚类结果的可视化，这也是自组织特征映射得到广泛应用的原因之一。     

基于SOFM网络的聚类分析

基于自组织特征映射网络的聚类分析，是在神经网络基础上发展起来的一种新的非监督聚类方法，分析了基于自组织特征映射网络聚类的学习过程，分析了权系数自组织过程中邻域函数和学习步长的一般取值问题，给出了基于自组织特征映射网络聚类实现的具体算法，并通过实际示例测试，证实了算法的正确性。     

基于GHSOM网络的时间序列聚类方法

提出了一种基于增长型分层自组织映射(GHSOM)的时间序列聚类方法，给出了该方法的基本原理和具体算法步骤，对实测时间序列数据进行了聚类验证和分析。研究结果表明，增长型分层自组织映射能根据对象特征无监督地对时间序列进行正确聚类，由于具有动态增长及分层特性，能分析对象内在的层次结构并实现由粗到精的聚类，可以扩展应用于大型乃至巨量时间序列数据库的模式发现。     

自组织特征映射网络在故障诊断中的应用

运用自组织特征映射神经网络的工作原理和具体实现算法进行故障诊断分析,在对已有神经网络聚类分析方法概括和总结的基础上,结合实验数据、仿真数据对自组织特征映射算法故障模型诊断进行研究,得出了有意义的结论.     

一种高效的自组织特征映射图的初始化方法

自组织特征映射图算法(SOFM,self-organizing Feature Map)在模式识别中有着广泛的应用.本文首先讨论了网络结构的初始化设置对自组织特征映射图构造的影响以及加速SOFM网络学习训练过程的主要方法,然后提出一种从边界到中心的自组织特征映射图初始化方法,该方法形成的自组织特征映射图能够真实地表示输入样本内在关系,大大减少学习训练次数,从而有效改进了传统的SOFM算法.     

基于核的双自组织特征映射网络在预测中的应用

提出了一种基于核的双自组织特征映射网络.该网络通过同时使用两个相关的映射网络扩展了原有的自组织神经网络,针对自组织特征映射网络容易受到高噪声的影响,将核学习的方法应用于自组织映射聚类中,以核函数代替了原始数据在特征空间中映射值的内积,传统的SOM算法使用的是欧氏距离,而KSOM通过使用不同的核函数为原始空间诱导出不同的欧式距离,这样就提高了算法的鲁棒性.将改进后的神经网络用于金融时间序列的预测,其实验结果表明,改进后的神经网络具有较强的鲁棒性.     

SOM integrated with CCA for the feature map and classification of complex chemical patterns

Considering that the two-dimensional (2D) feature map of the high-dimensional chemical patterns can more concisely and efficiently represent the pattern characteristic, a new procedure integrating self-organizing map (SOM) networks with correlative component analysis (CCA) is proposed. Firstly, CCA was used to identify the most important classification characteristics (CCs) from the original high-dimensional chemical pattern information. Then, the SOM maps the first several CCs, which include the most useful information for pattern classification, onto a 2D plane, on which the pattern classification feature is concisely represented. To improve the learning efficiency of SOM networks, two new algorithms for dynamically adjusting the learning rate and the range of neighborhood around the winning unit were further worked out. Besides, a convenient method for detecting the topologic nature of SOM results was proposed. Finally, a typical example of mapping two classes natural spearmint essence was employed to verify the effectiveness of the new approach. The feature-topology-preserving (FTP) map obtained can well represent the classification of original patterns and is much better than what obtained by SOM alone.     

A method for condition monitoring and fault diagnosis in electromechanical system

Condition monitoring of electrical machines has received considerable attention in recent years. Many monitoring techniques have been proposed for electrical machine fault detection and localization. In this paper, the feasibility of using a nonlinear feature extraction method noted as Kernel independent component analysis (KICA) is studied and it is applied in self-organizing map to classify the faults of induction motor. In nonlinear feature extraction, we employed independent component analysis (ICA) procedure and adopted the kernel trick to nonlinearly map the Gaussian chirplet distributions into a feature space. First, the adaptive Gaussian chirplet distributions are mapped into an implicit feature space by the kernel trick, and then ICA is performed to extract nonlinear independent components of the Gaussian chirplet distributions. A thorough laboratory study shows that the diagnostic methods provide accurate diagnosis, high sensitivity with respect to faults, and good diagnostic resolution.     

Spatio-Temporal Analysis with the Self-Organizing Feature Map

Spatio-temporal pattern recognition problems are particularly challenging. They typically involve detecting change that occurs over time in two-dimensional patterns. Analytic techniques devised for temporal data must take into account the spatial relationships among data points. An artificial neural network known as the self-organizing feature map (SOM) has been used to analyze spatial data. This paper further investigates the use of the SOM with spatio-temporal pattern recognition. The principles of the two-dimensional SOM are developed into a novel three-dimensional network and experiments demonstrate that (i) the three-dimensional network makes a better topological ordering and (ii) there is a difference in terms of the spatio-temporal analysis that can be made with the three-dimensional network. Received 21 October 1999 / Revised 11 February 2000 / Accepted 2 May 2000     

Monitoring of quality-relevant and quality-irrelevant blocks with characteristic-similar variables based on self-organizing map and kernel approaches

Variables in quality-related process monitoring can be divided into quality-relevant and quality-irrelevant groups depending on the correlation with the quality indicator. These variables can also be separated into multiple sets in which variables are closely relevant to one another because of the interdependence of the process. Block monitoring with reasonable variable partition and reliable model can distinguish quality-related and quality-unrelated faults and improve monitoring performance. A block monitoring method based on self-organizing map (SOM) and kernel approaches is proposed. After collecting and normalizing the sample data including process variables and quality ones, the data matrix is transposed. The inverted samples are used as the input of SOM, and variables with the same behavioral characteristic and a close correlation are topologically mapped in a similar area. Accordingly, samples can be visually blocked into quality-relevant and independent subspaces. Given the nonlinearity of industrial process, kernel partial least squares (KPLS) and kernel principal component analysis (KPCA) are employed to monitor the two types of blocks. The information provided by fault detection can reveal the effects on quality indicators and the location of faults. Finally, the effectiveness of SOM-KPLS/KPCA is evaluated using a numerical example and the Tennessee–Eastman process.     

基于变量相关性的多元时间序列特征表示

针对高维特性对多元时间序列数据挖掘过程和结果的影响,以及传统主成分分析方法在多元时间序列数据特征表示上的局限性,提出一种基于变量相关性的多元时间序列数据特征表示方法。通过协方差矩阵描述每个多元时间序列的分布特征和变量相关关系,利用主成分分析方法对综合协方差矩阵进行主元分析,进而实现多元时间序列的数据降维和特征表示。实验结果表明,所提出的方法不仅能提高多元时间序列数据挖掘的质量,还可以对不等长多元时间序列进行快速有效的挖掘。     

A novel multiset integrated canonical correlation analysis framework and its application in feature fusion

Multiset canonical correlation analysis (MCCA) is difficult to effectively express the integrated correlation among multiple feature vectors in feature fusion. Thus, this paper firstly presents a novel multiset integrated canonical correlation analysis (MICCA) framework. The MICCA establishes a discriminant correlation criterion function of multi-group variables based on generalized correlation coefficient. The criterion function can clearly depict the integrated correlation among multiple feature vectors. Then the paper presents a multiple feature fusion theory and algorithm using the MICCA method. The detailed process of the algorithm is as follows: firstly, extract multiple feature vectors from the same patterns by using different feature extraction methods; then extract multiset integrated canonical correlation features using MICCA; finally form effective discriminant feature vectors through two given feature fusion strategies for pattern classification. The multi-group feature fusion method based on MICCA not only achieves the aim of feature fusion, but also removes the redundancy between features. The experiment results on CENPARMI handwritten Arabic numerals and UCI multiple features database show that the MICCA method has better recognition rates and robustness than the fusion methods based on canonical correlation analysis (CCA) and MCCA.     

基于特征选择的二维主分量分析

提出了一种基于分类性能的二维主分量特征选择方法.即将二维主分量分析中图像总体散布矩阵的特征向量在二维线性鉴别分析的目标函数上进行投影,并选择分类性更好的特征向量进行投影.另外,为了保持原有的二维主分量分析主特征的优点,对最后的投影特征向量进行组合,也就是最后的投影特征向量选取对图像重建和图像分类分别起着重要作用的特征进行组合.在XM2VTS标准人脸库上的试验结果表明,所提出的方法融合了两种具有互补性的图像并行特征,在识别性能上优于传统的二维主分量分析方法.     

Improved support vector classification using PCA and ICA feature space modification

An approach that unifies subspace feature selection and optimal classification is presented. Independent component analysis (ICA) and principal component analysis (PCA) provide a maximally variant or statistically independent basis for pattern recognition. A support vector classifier (SVC) provides information about the significance of each feature vector. The feature vectors and the principal and independent component bases are modified to obtain classification results which provide lower classification error and better generalization than can be obtained by the SVC on the raw data and its PCA or ICA subspace representation. The performance of the approach is demonstrated with artificial data sets and an example of face recognition from an image database.     

A logic programming approach to cartographic map overlay

Cartographic map overlay is the process of superimposing two maps into one to convey information in spatial correlation. A map refers to one in vector representation: a two-dimensional spatial data structure of nodes, chains, and polygons. We present a map overlay system developed in Prolog. The system adopts a relational approach to data structuring. We represent geometric entities and their relationships as facts, and encode geometry algorithms in the rules. Set-based operations perform data processing. To speed up the search for chain intersections, a uniform rectangular grid is imposed over the object space for spatial sorting by distribution. We sort out potentially intersecting edge segments to those occupying some common grid cell. Each bucket, if non-empty, is implemented as a Prolog fact identifying the grid cell for random access. Geometric intersections are calculated using exact rational arithmetic implemented in Prolog. Numerical accuracy is preserved and we can identify all the special cases of tangent conditions. We can then guarantee topological consistency, and stability in the process of map overlay is therefore achieved.     

基于全局和局部特征融合的显著性提取方法

显著性提取方法在图像处理、计算机视觉领域有着广泛的应用.然而,基于全局特征和基于局部特征的显著性区域提取算法存在各自的缺点,为此本文提出了一种融合全局和局部特征的显著性提取算法.首先,对图像进行不重叠地分块,当每个图像块经过主成分分析(Principle component analysis,PCA)映射到高维空间后,根据孤立的特征点对应显著性区域的规律得到基于全局特征的显著图;其次,根据邻域内中心块与其他块的颜色不相似性得到基于局部特征的显著图;最后,按照贝叶斯理论将这两个显著图融合为最终的显著图.在公认的三个图像数据库上的仿真实验验证了所提算法在显著性提取和目标分割上比其他先进算法更有效.