In chapter 2, a method is presented which gets radar target high dimensional structure information from one dimensional range profiles based on Independent Component Analysis ( ICA).

The ICA, kernel principle component analysis ( KPCA) and kernel ICA ( KICA) were used to extract features.

本文分别利用ICA、核主成分分析（KPCA）以及核独立成分分析（KICA）对AE参数进行特征提取。

The analysis of functional magnetic resonance imaging ( fMRI) signals using independent component analysis ( ICA) has been a hotspot in the recent years.

独立成分分析（ICA）在功能核磁共振成像（fMRI）技术中的应用是近年来人们关注的一个热点。

Therefore, after analysis by ICA the independent component factors determine the model input nodes of timing BP neural network, the input dimension is reduced, and the structure of neural network is optimized.

因此经过独立成分分析优化时序BP神经网络预测模型的输入节点因子，降低了输入维数，优化了神经网络结构。

To improve solid velocity measurement precision, the essential method for processing two-phase flow signals is described based on independent component analysis ( ICA).

为了提高固体速度测量精度，提供一种基于独立成分分析（ICA）处理两相流信号的基本方法。

Independent component analysis ( ICA) is an approach first used in blind source separation and now widely applied in pattern recognition domain.

独立成分分析（ICA）最早应用于盲信号分离，近年来也广泛地应用于模式识别领域。

Independent Component Analysis ( ICA) is a statistical technique that attempts to decompose multivariate data into many statistically independent components.

独立成分分析（ICA）技术试图将多维数据分解成若干个相互统计独立的分量。

Independent component analysis ( ICA) are generally used for feature extraction, process monitoring, but rarely used for classification.

独立成分分析方法（ICA）一般用于特征提取、过程监控，很少用于分类。

This paper proposes the face recognition method based on principle component analysis ( PCA) and independent component analysis ( ICA).

提出利用主成分分析（PCA）和独立成分分析（ICA）相结合的方法对人脸进行识别。

Independent component analysis ( ICA) is a recently devel-oped method based on higher order statistical analysis.

独立成分分析方法（ICA）是近几年才发展起来的一种新的基于信号高阶统计特性的分析方法。

The results indicate that ICA is an effective method for the analysis of fMRI data.

结果表明独立成分分析是进行fMRI数据分析的一种有效手段。

Applying independent component analysis to fMRI data analysis

独立成分分析应用于fMRI数据研究

Then the neural network is used for classification. Both the back-propagation algorithm based neural network and RBF neural network are applied to the classification.

为了提高分类精度，采用神经网络对由独立成分分析提取出的光谱特征进行分类，分别将BP神经网络和径向基神经网络应用于高光谱数据分类问题。

Mixed LFM Signal Detection Based on Independent Component Analysis

基于独立成分分析的混合LFM信号检测

Independent Component Analysis Based on Instantaneous Power of fMRI Data

基于fMRI瞬时功率的独立成分分析

An independent component analysis algorithm for blind separation of fMRI signals

fMRI信号盲分离的一种独立成分分析算法

In process industry, the key to ensure safety is the effective process monitoring. PCA and ICA method are the usual data-based process monitoring methods.

在工业过程中，保证生产正常运行的关键是对运行过程的准确有效的实时监测，主元分析方法和独立成分分析方法等是常用的基于数据驱动的过程监测方法。

ICA is used to extract statistical independent feature and a good result can be received by selecting right feature numbers.

独立成分分析能更本质地描述图像特征，通过选择合适的特征个数达到较高的识别准确率。

Some of face recognition methods based on Principal Component Analysis ( PCA), Two-dimensional Principal Component Analysis ( 2DPCA) and Fisher's Linear Discriminant Analysis ( FLDA) are comparatively studied in this paper.

对基于主元分析（PCA）、二维主元分析（2DPCA）和Fisher线性判别分析（FLDA）的人脸识别方法进行了比较研究。

In this paper, a new music classification method is presented. The LDA ( Linear Discriminative Analysis) and SVMs ( Support Vector Machine) are used in this method to classify the music data accurately.

提出了一种新的音乐分类方法，该方法使用线性判别分析（LDA）和支持向量机（SVMs）对音乐数据进行分类。

First, eyes and mouths are segmented from the facial expression image and variant moments and singular value feature vectors of eyes and mouth are extracted. Then Fisher linear discriminant analysis is used to find a set of optimal feature vectors.

先对面部表情图像进行了分割，得到眼睛和嘴巴区域，然后分别对眼睛和嘴巴区域提取不变矩和奇异值特征向量，并进行Fisher线性判别分析，最后训练了支持向量机分类器。