dimensionality reduction

Dimensionality reduction process can be divided into two steps. The first step is to select pivot points to determine the coordinate axes in a target space.

降维过程主要分为两步，首先通过选择枢纽点确定目标空间中的坐标轴，然后计算数据集中的样本点在这些坐标轴上的投影坐标。

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As a result, using dimensionality reduction techniques for data processing becomes extremely important.

因此，利用降维技术对高维数据进行处理就显得尤为重要。

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A new feature extraction method is adopted in classification and recognition of distilled spirit using principle component analysis ( PCA), which provides a solution to the construction of transform matrix for dimensionality reduction.

在用PCA（主成分分析）方法分类和识别白酒时，提出一种新的特征选择方法，解决了降维变换矩阵的组成问题。

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The detailed content can be illustrated as follows. Firstly, already proposed dimensionality reduction cause unboundedness of searching space, so subsequence similarity search algorithm can not effectively filter the point in the index.

首先，原有降维技术造成序列数据搜索空间无界性，使得子序列相似查询算法在索引遍历时不能充分实现点过滤。

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The experiment on artificial data and UCI data show that the proposed algorithms have greatly improved in accuracy comparing to the original ones, and is an effective tool for nonlinear dimensionality reduction.

在人工数据与UCI数据上的实验表明，我们提出的方案较原始算法在精确率上有较大提高，是有效的非线性降维工具。

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With the fast development of computer vision and pattern recognition, in lots of applications, more and more data dimensionality reduction problems are involved.

随着近年来模式识别和计算机视觉领域的快速发展，越来越多的应用涉及到了高维数据的降维问题。

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Nonlinear dimensionality reduction in the analysis of high dimensional medical data

非线性降维在高维医学数据处理中的应用

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The boundary element method ( BEM) is a widely used method because of its accuracy and dimensionality reduction.

边界元法（BEM）是一种应用广泛的求解偏微分方程的方法，它具有精度高，降维等特点。

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This paper compares the most popular dimensionality reduction and pattern classification methods used in criminal suspect face recognition.

比较了犯罪嫌疑人脸部识别中常用的数据降维和模式分类方法。

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Design and Simulation for Dimensionality Reduction Observer of Position Servocontrol System

位置随动控制系统的降维观测器设计与仿真

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Among them, principal component analysis ( PCA) and linear discriminant analysis ( LDA) are more often used in dimensionality reduction.

其中的主成分分析（PCA）和线性判别分析（LDA）经常被用于特征降维。

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A novel method for dimensionality reduction of kernel matrix is presented.

提出了基于聚类的核矩阵维度缩减技术.

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Frequent item sets based dimensionality reduction algorithm in data mining research

基于频繁项集的降维在数据挖掘中的应用

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Dimensionality Reduction based on the combination of Graph Embedding and MMI

基于图嵌入和最大互信息组合的降维

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In order to reduce computational effort , piecemeal dimensionality reduction Newton method was used to solve model.

为减少计算量, 采用分块牛顿降维算法求解.

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Self Organizing Feature Maps for Categorical Data Self Organizing Feature Maps ( SOM) has been used as a tool for data clustering and dimensionality reduction.

作为一种优良的聚类和降维工具，自组织神经网络SOM（SelfOrganizingFeatureMaps）已经得到广泛应用。

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