主元

Using spectral decomposition of a matrix and multi-scale representation of spectral as well as multi-scale transform of a signal, a quasi multi-scale PCA method is proposed to analyze the reason why multi-scale detection method does well than single scale method.

基于矩阵的谱分解、信号的多尺度变换及谱的多尺度表示，提出一种拟多尺度主元分析方法，从机理上分析了多尺度检测方法优于传统单尺度检测方法的内在原因。

PCA is to perform the variable transformation, whereas factor analysis needs to form a factor model.

主元分析只是进行一定的变量变换，而因子分析则需构成因子模型。

Canonical variate analysis is applied to identify a state space model in linear space and state information is extracted.

在CSTR系统上的仿真结果表明，KCVA方法比主元分析法和CVA方法能更灵敏地检测到故障的发生，更有效地监控过程变化。

Instead of statistical description, a feature subspace with the base of some significant eigen vector extracted from covariance matrix is constructed to describe speech feature distribution of speaker. Distance metrics for measuring distance between input feature vector and subspace are also proposed for pattern matching.

说话人语音训练样本提取特征后在语音特征观察空间形成具有一定散度的分布，根据主元分析原理和分布散度提取主要散度本征向量作为基底构成说话人语音特征子空间，并通过测试语音特征矢量与子空间的距离测度进行模式匹配。

Compared to state-of-the-art methods based on Kohonen's adaptive-subspace self-organizing map, our method avoids confusion of data in the manifold representation. Each neuron in the network approximately learns the mean vector and principal subspace of the data in its local region. The data representation is therefore more discernable.

与现有的基于Kohonen的自适应子空间自组织映射网络(Adaptive-subspace self-organizing map， ASSOM)方法相比较，本文方法有效地克服了流形表达中出现的数据混淆现象，网络中各神经元渐近学习各自区域内样本数据的平均向量和主元子空间，数据表达更加清晰可辨。

If the target is a slicing: The primary expression in the reference is evaluated.

如果目标是一片断：引用中的主元表达式被求值。

The main contributions of this thesis are as follows:(1)To address the problem of kernel parameterσselection and regulation of the decision boundary in SVDD algorithm,this thesis proposes a new kernel parameter optimization method based on the spheral distribution of samples in kernel space and regulation of the decision boundary method based on KPCA(Kernel Principal Component Analysis).

本论文主要以支持向量数据描述(Support Vector Data Description，SVDD)与随机森林(Random Forests，RF)模式识别工具为基础，对流程工业在线故障诊断的若干问题进行研究，其具体内容如下：(1)针对SVDD的核参数σ优化及其决策边界规整问题，提出了基于核样本球形分布的核参数优化方法与基于核主元分析(Kernel Principal ComponentAnalysis，KPCA)的SVDD决策边界规整方法。

Used fault diagnosis method based on Principal Components Analysis to research the fault monitoring and diagnosis of Floating Production Storage and Off-loading system.

应用基于主元分析的故障诊断方法对浮式油轮生产储油卸油系统进行故障检测与诊断研究。

The vinegars could be identified according to their brands, kinds, and the total acidity of the vinegars indicated by the Principal Component Analysis.

主元分析表明不同的食醋在品牌、种类、酸度等方面具有一定的相似性。

Deletion of attribute references, subscriptions and slicings is passed to the primary object involved; deletion of a

对于属性引用，下标和片断的删除会作用到相关的主元对象，对片断的删除一般等价于对该片断赋予相应

As she looked at Warrington's manly face, and dark, melancholy eyes, she had settled in her mind that he must have been the victim of an unhappy attachment.

每逢看到沃林顿那刚毅的脸，那乌黑、忧郁的眼睛，她便会相信，他一定作过不幸的爱情的受害者。

Maybe they'll disappear into a pothole.

也许他们将在壶穴里消失